{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbece81f4b1399e6",
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import argparse\n",
    "from model import *\n",
    "from metric import *\n",
    "from sklearn import metrics\n",
    "import torch.nn.functional as F\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57be0adc-e87b-4d7e-bd0e-01d68e204fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "args Namespace(seed=0, epochs=500, weight_decay=0.0005, dropout=0.1, tot_updates=1000, warmup_updates=400, peak_lr=0.001, end_lr=0.0001, pe_dim=15, hops=7, graphformer_layers=1, n_heads=8, node_input=64, node_hidden=128, node_output=64, ffn_dim=256, GCNII_layers=20)\n",
      "元素为1的个数： 2555\n",
      "Dis_adj 中值为 1 的元素个数: 3146\n",
      "Meta_adj 中值为 1 的元素个数: 561581\n",
      "seed=0, evaluating metabolite-disease....\n",
      "------this is 1th cross validation------\n",
      "total params: 307522\n"
     ]
    },
   
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6975 Acc: 0.5039 Pre: 0.5020 Recall: 1.0000 F1: 0.6684 Train AUC: 0.5215 Val AUC: 0.5303 Val PRC: 0.5952 Time: 0.89\n",
      "Epoch: 2 Train Loss: 0.7002 Acc: 0.5020 Pre: 0.5010 Recall: 0.9980 F1: 0.6671 Train AUC: 0.5239 Val AUC: 0.5262 Val PRC: 0.5381 Time: 0.27\n",
      "Epoch: 3 Train Loss: 0.6672 Acc: 0.5587 Pre: 0.5338 Recall: 0.9276 F1: 0.6776 Train AUC: 0.6900 Val AUC: 0.6724 Val PRC: 0.6854 Time: 0.23\n",
      "Epoch: 4 Train Loss: 0.6905 Acc: 0.5010 Pre: 0.5005 Recall: 0.9980 F1: 0.6667 Train AUC: 0.5589 Val AUC: 0.5760 Val PRC: 0.6192 Time: 0.24\n",
      "Epoch: 5 Train Loss: 0.6794 Acc: 0.5509 Pre: 0.5278 Recall: 0.9667 F1: 0.6828 Train AUC: 0.6234 Val AUC: 0.6133 Val PRC: 0.6146 Time: 0.23\n",
      "Epoch: 6 Train Loss: 0.6924 Acc: 0.5098 Pre: 0.5050 Recall: 0.9980 F1: 0.6706 Train AUC: 0.5527 Val AUC: 0.5453 Val PRC: 0.5763 Time: 0.23\n",
      "Epoch: 7 Train Loss: 0.6668 Acc: 0.5881 Pre: 0.5527 Recall: 0.9237 F1: 0.6916 Train AUC: 0.6706 Val AUC: 0.6846 Val PRC: 0.6901 Time: 0.24\n",
      "Epoch: 8 Train Loss: 0.6647 Acc: 0.6027 Pre: 0.5624 Recall: 0.9256 F1: 0.6997 Train AUC: 0.6679 Val AUC: 0.7009 Val PRC: 0.7078 Time: 0.24\n",
      "Epoch: 9 Train Loss: 0.6585 Acc: 0.6184 Pre: 0.5752 Recall: 0.9061 F1: 0.7036 Train AUC: 0.7039 Val AUC: 0.6928 Val PRC: 0.6800 Time: 0.23\n",
      "Epoch: 10 Train Loss: 0.6454 Acc: 0.6409 Pre: 0.5898 Recall: 0.9256 F1: 0.7205 Train AUC: 0.7393 Val AUC: 0.7426 Val PRC: 0.7210 Time: 0.23\n",
      "Epoch: 11 Train Loss: 0.6607 Acc: 0.6067 Pre: 0.5653 Recall: 0.9237 F1: 0.7013 Train AUC: 0.6766 Val AUC: 0.6895 Val PRC: 0.6798 Time: 0.23\n",
      "Epoch: 12 Train Loss: 0.6270 Acc: 0.6693 Pre: 0.6075 Recall: 0.9569 F1: 0.7432 Train AUC: 0.7683 Val AUC: 0.7712 Val PRC: 0.7640 Time: 0.24\n",
      "Epoch: 13 Train Loss: 0.6349 Acc: 0.7074 Pre: 0.6480 Recall: 0.9080 F1: 0.7563 Train AUC: 0.7722 Val AUC: 0.7645 Val PRC: 0.7150 Time: 0.24\n",
      "Epoch: 14 Train Loss: 0.6334 Acc: 0.6184 Pre: 0.5698 Recall: 0.9667 F1: 0.7170 Train AUC: 0.7389 Val AUC: 0.7388 Val PRC: 0.7189 Time: 0.24\n",
      "Epoch: 15 Train Loss: 0.5938 Acc: 0.7720 Pre: 0.7132 Recall: 0.9100 F1: 0.7997 Train AUC: 0.8313 Val AUC: 0.8425 Val PRC: 0.8182 Time: 0.23\n",
      "Epoch: 16 Train Loss: 0.6144 Acc: 0.7065 Pre: 0.6354 Recall: 0.9687 F1: 0.7674 Train AUC: 0.7778 Val AUC: 0.7855 Val PRC: 0.7515 Time: 0.24\n",
      "Epoch: 17 Train Loss: 0.6038 Acc: 0.7231 Pre: 0.6570 Recall: 0.9335 F1: 0.7712 Train AUC: 0.7940 Val AUC: 0.8008 Val PRC: 0.7832 Time: 0.23\n",
      "Epoch: 18 Train Loss: 0.5935 Acc: 0.7074 Pre: 0.6373 Recall: 0.9628 F1: 0.7670 Train AUC: 0.8022 Val AUC: 0.8012 Val PRC: 0.7771 Time: 0.24\n",
      "Epoch: 19 Train Loss: 0.5914 Acc: 0.7221 Pre: 0.6553 Recall: 0.9374 F1: 0.7713 Train AUC: 0.7870 Val AUC: 0.7855 Val PRC: 0.7596 Time: 0.26\n",
      "Epoch: 20 Train Loss: 0.5798 Acc: 0.7906 Pre: 0.7399 Recall: 0.8963 F1: 0.8106 Train AUC: 0.8207 Val AUC: 0.8361 Val PRC: 0.7902 Time: 0.29\n",
      "Epoch: 21 Train Loss: 0.5725 Acc: 0.7603 Pre: 0.6985 Recall: 0.9159 F1: 0.7925 Train AUC: 0.8176 Val AUC: 0.8246 Val PRC: 0.7980 Time: 0.31\n",
      "Epoch: 22 Train Loss: 0.5590 Acc: 0.7994 Pre: 0.7492 Recall: 0.9002 F1: 0.8178 Train AUC: 0.8336 Val AUC: 0.8447 Val PRC: 0.8307 Time: 0.30\n",
      "Epoch: 23 Train Loss: 0.5386 Acc: 0.7886 Pre: 0.7504 Recall: 0.8650 F1: 0.8036 Train AUC: 0.8598 Val AUC: 0.8558 Val PRC: 0.8325 Time: 0.28\n",
      "Epoch: 24 Train Loss: 0.5240 Acc: 0.8239 Pre: 0.8048 Recall: 0.8552 F1: 0.8292 Train AUC: 0.8743 Val AUC: 0.8803 Val PRC: 0.8673 Time: 0.25\n",
      "Epoch: 25 Train Loss: 0.5198 Acc: 0.8112 Pre: 0.8034 Recall: 0.8239 F1: 0.8135 Train AUC: 0.8743 Val AUC: 0.8677 Val PRC: 0.8539 Time: 0.25\n",
      "Epoch: 26 Train Loss: 0.5043 Acc: 0.8200 Pre: 0.8277 Recall: 0.8082 F1: 0.8178 Train AUC: 0.8874 Val AUC: 0.8807 Val PRC: 0.8750 Time: 0.25\n",
      "Epoch: 27 Train Loss: 0.5106 Acc: 0.8307 Pre: 0.8073 Recall: 0.8689 F1: 0.8369 Train AUC: 0.8714 Val AUC: 0.8881 Val PRC: 0.8660 Time: 0.26\n",
      "Epoch: 28 Train Loss: 0.4881 Acc: 0.8209 Pre: 0.8060 Recall: 0.8454 F1: 0.8252 Train AUC: 0.8895 Val AUC: 0.8901 Val PRC: 0.8836 Time: 0.24\n",
      "Epoch: 29 Train Loss: 0.4743 Acc: 0.8356 Pre: 0.8672 Recall: 0.7926 F1: 0.8282 Train AUC: 0.8938 Val AUC: 0.8995 Val PRC: 0.8986 Time: 0.27\n",
      "Epoch: 30 Train Loss: 0.4511 Acc: 0.8288 Pre: 0.8360 Recall: 0.8180 F1: 0.8269 Train AUC: 0.9031 Val AUC: 0.9113 Val PRC: 0.9154 Time: 0.23\n",
      "Epoch: 31 Train Loss: 0.4510 Acc: 0.8346 Pre: 0.8654 Recall: 0.7926 F1: 0.8274 Train AUC: 0.9095 Val AUC: 0.9073 Val PRC: 0.9070 Time: 0.23\n",
      "Epoch: 32 Train Loss: 0.4426 Acc: 0.8386 Pre: 0.8879 Recall: 0.7750 F1: 0.8276 Train AUC: 0.8992 Val AUC: 0.9092 Val PRC: 0.9181 Time: 0.23\n",
      "Epoch: 33 Train Loss: 0.4137 Acc: 0.8297 Pre: 0.8518 Recall: 0.7984 F1: 0.8242 Train AUC: 0.9109 Val AUC: 0.9119 Val PRC: 0.9180 Time: 0.25\n",
      "Epoch: 34 Train Loss: 0.4187 Acc: 0.8386 Pre: 0.8794 Recall: 0.7847 F1: 0.8294 Train AUC: 0.9080 Val AUC: 0.9145 Val PRC: 0.9130 Time: 0.24\n",
      "Epoch: 35 Train Loss: 0.3981 Acc: 0.8444 Pre: 0.8621 Recall: 0.8200 F1: 0.8405 Train AUC: 0.9158 Val AUC: 0.9077 Val PRC: 0.9163 Time: 0.23\n",
      "Epoch: 36 Train Loss: 0.3987 Acc: 0.8425 Pre: 0.9014 Recall: 0.7691 F1: 0.8300 Train AUC: 0.9106 Val AUC: 0.9122 Val PRC: 0.9223 Time: 0.24\n",
      "Epoch: 37 Train Loss: 0.3949 Acc: 0.8356 Pre: 0.8704 Recall: 0.7886 F1: 0.8275 Train AUC: 0.9086 Val AUC: 0.9026 Val PRC: 0.9152 Time: 0.24\n",
      "Epoch: 38 Train Loss: 0.3812 Acc: 0.8376 Pre: 0.8791 Recall: 0.7828 F1: 0.8282 Train AUC: 0.9110 Val AUC: 0.9100 Val PRC: 0.9242 Time: 0.24\n",
      "Epoch: 39 Train Loss: 0.3444 Acc: 0.8483 Pre: 0.8397 Recall: 0.8611 F1: 0.8502 Train AUC: 0.9333 Val AUC: 0.9317 Val PRC: 0.9398 Time: 0.24\n",
      "Epoch: 40 Train Loss: 0.3567 Acc: 0.8483 Pre: 0.9178 Recall: 0.7652 F1: 0.8346 Train AUC: 0.9197 Val AUC: 0.9138 Val PRC: 0.9250 Time: 0.25\n",
      "Epoch: 41 Train Loss: 0.3467 Acc: 0.8503 Pre: 0.8552 Recall: 0.8434 F1: 0.8493 Train AUC: 0.9287 Val AUC: 0.9252 Val PRC: 0.9278 Time: 0.23\n",
      "Epoch: 42 Train Loss: 0.3490 Acc: 0.8474 Pre: 0.8487 Recall: 0.8454 F1: 0.8471 Train AUC: 0.9228 Val AUC: 0.9272 Val PRC: 0.9313 Time: 0.23\n",
      "Epoch: 43 Train Loss: 0.3272 Acc: 0.8571 Pre: 0.9065 Recall: 0.7965 F1: 0.8479 Train AUC: 0.9317 Val AUC: 0.9260 Val PRC: 0.9316 Time: 0.24\n",
      "Epoch: 44 Train Loss: 0.3325 Acc: 0.8571 Pre: 0.8842 Recall: 0.8219 F1: 0.8519 Train AUC: 0.9298 Val AUC: 0.9244 Val PRC: 0.9347 Time: 0.23\n",
      "Epoch: 45 Train Loss: 0.3329 Acc: 0.8562 Pre: 0.8714 Recall: 0.8356 F1: 0.8531 Train AUC: 0.9309 Val AUC: 0.9296 Val PRC: 0.9363 Time: 0.23\n",
      "Epoch: 46 Train Loss: 0.3225 Acc: 0.8630 Pre: 0.8809 Recall: 0.8395 F1: 0.8597 Train AUC: 0.9330 Val AUC: 0.9322 Val PRC: 0.9421 Time: 0.24\n",
      "Epoch: 47 Train Loss: 0.3200 Acc: 0.8679 Pre: 0.9000 Recall: 0.8278 F1: 0.8624 Train AUC: 0.9360 Val AUC: 0.9287 Val PRC: 0.9364 Time: 0.23\n",
      "Epoch: 48 Train Loss: 0.3219 Acc: 0.8796 Pre: 0.9217 Recall: 0.8297 F1: 0.8733 Train AUC: 0.9346 Val AUC: 0.9397 Val PRC: 0.9485 Time: 0.24\n",
      "Epoch: 49 Train Loss: 0.3045 Acc: 0.8679 Pre: 0.9141 Recall: 0.8121 F1: 0.8601 Train AUC: 0.9400 Val AUC: 0.9358 Val PRC: 0.9438 Time: 0.23\n",
      "Epoch: 50 Train Loss: 0.3118 Acc: 0.8630 Pre: 0.9059 Recall: 0.8102 F1: 0.8554 Train AUC: 0.9377 Val AUC: 0.9331 Val PRC: 0.9391 Time: 0.25\n",
      "Epoch: 51 Train Loss: 0.2941 Acc: 0.8816 Pre: 0.9149 Recall: 0.8415 F1: 0.8767 Train AUC: 0.9441 Val AUC: 0.9404 Val PRC: 0.9463 Time: 0.23\n",
      "Epoch: 52 Train Loss: 0.3099 Acc: 0.8767 Pre: 0.9231 Recall: 0.8219 F1: 0.8696 Train AUC: 0.9382 Val AUC: 0.9345 Val PRC: 0.9453 Time: 0.23\n",
      "Epoch: 53 Train Loss: 0.2980 Acc: 0.8787 Pre: 0.9234 Recall: 0.8258 F1: 0.8719 Train AUC: 0.9424 Val AUC: 0.9376 Val PRC: 0.9468 Time: 0.23\n",
      "Epoch: 54 Train Loss: 0.2898 Acc: 0.8611 Pre: 0.8625 Recall: 0.8591 F1: 0.8608 Train AUC: 0.9464 Val AUC: 0.9388 Val PRC: 0.9466 Time: 0.24\n",
      "Epoch: 55 Train Loss: 0.3058 Acc: 0.8796 Pre: 0.9217 Recall: 0.8297 F1: 0.8733 Train AUC: 0.9409 Val AUC: 0.9432 Val PRC: 0.9507 Time: 0.23\n",
      "Epoch: 56 Train Loss: 0.2973 Acc: 0.8836 Pre: 0.9100 Recall: 0.8513 F1: 0.8797 Train AUC: 0.9434 Val AUC: 0.9418 Val PRC: 0.9498 Time: 0.23\n",
      "Epoch: 57 Train Loss: 0.2971 Acc: 0.8757 Pre: 0.8871 Recall: 0.8611 F1: 0.8739 Train AUC: 0.9430 Val AUC: 0.9410 Val PRC: 0.9449 Time: 0.23\n",
      "Epoch: 58 Train Loss: 0.2879 Acc: 0.8728 Pre: 0.8994 Recall: 0.8395 F1: 0.8684 Train AUC: 0.9470 Val AUC: 0.9436 Val PRC: 0.9509 Time: 0.23\n",
      "Epoch: 59 Train Loss: 0.2885 Acc: 0.8806 Pre: 0.8867 Recall: 0.8728 F1: 0.8797 Train AUC: 0.9467 Val AUC: 0.9470 Val PRC: 0.9525 Time: 0.24\n",
      "Epoch: 60 Train Loss: 0.2842 Acc: 0.8816 Pre: 0.9239 Recall: 0.8317 F1: 0.8754 Train AUC: 0.9462 Val AUC: 0.9447 Val PRC: 0.9523 Time: 0.23\n",
      "Epoch: 61 Train Loss: 0.2871 Acc: 0.8855 Pre: 0.8940 Recall: 0.8748 F1: 0.8843 Train AUC: 0.9475 Val AUC: 0.9492 Val PRC: 0.9540 Time: 0.23\n",
      "Epoch: 62 Train Loss: 0.2743 Acc: 0.8865 Pre: 0.9140 Recall: 0.8532 F1: 0.8826 Train AUC: 0.9513 Val AUC: 0.9513 Val PRC: 0.9569 Time: 0.23\n",
      "Epoch: 63 Train Loss: 0.2897 Acc: 0.8845 Pre: 0.9119 Recall: 0.8513 F1: 0.8806 Train AUC: 0.9464 Val AUC: 0.9510 Val PRC: 0.9520 Time: 0.24\n",
      "Epoch: 64 Train Loss: 0.2861 Acc: 0.8855 Pre: 0.8745 Recall: 0.9002 F1: 0.8872 Train AUC: 0.9488 Val AUC: 0.9505 Val PRC: 0.9552 Time: 0.23\n",
      "Epoch: 65 Train Loss: 0.2896 Acc: 0.8943 Pre: 0.9242 Recall: 0.8591 F1: 0.8905 Train AUC: 0.9452 Val AUC: 0.9525 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 66 Train Loss: 0.2725 Acc: 0.8924 Pre: 0.9084 Recall: 0.8728 F1: 0.8902 Train AUC: 0.9526 Val AUC: 0.9517 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 67 Train Loss: 0.2770 Acc: 0.8914 Pre: 0.9310 Recall: 0.8454 F1: 0.8862 Train AUC: 0.9510 Val AUC: 0.9520 Val PRC: 0.9557 Time: 0.23\n",
      "Epoch: 68 Train Loss: 0.2807 Acc: 0.8836 Pre: 0.9066 Recall: 0.8552 F1: 0.8802 Train AUC: 0.9497 Val AUC: 0.9509 Val PRC: 0.9568 Time: 0.24\n",
      "Epoch: 69 Train Loss: 0.2618 Acc: 0.8924 Pre: 0.9446 Recall: 0.8337 F1: 0.8857 Train AUC: 0.9553 Val AUC: 0.9530 Val PRC: 0.9525 Time: 0.24\n",
      "Epoch: 70 Train Loss: 0.2664 Acc: 0.8904 Pre: 0.9113 Recall: 0.8650 F1: 0.8876 Train AUC: 0.9544 Val AUC: 0.9526 Val PRC: 0.9573 Time: 0.23\n",
      "Epoch: 71 Train Loss: 0.2763 Acc: 0.8992 Pre: 0.9232 Recall: 0.8708 F1: 0.8963 Train AUC: 0.9514 Val AUC: 0.9550 Val PRC: 0.9537 Time: 0.23\n",
      "Epoch: 72 Train Loss: 0.2677 Acc: 0.9012 Pre: 0.9325 Recall: 0.8650 F1: 0.8975 Train AUC: 0.9566 Val AUC: 0.9563 Val PRC: 0.9574 Time: 0.23\n",
      "Epoch: 73 Train Loss: 0.2709 Acc: 0.8973 Pre: 0.9143 Recall: 0.8767 F1: 0.8951 Train AUC: 0.9537 Val AUC: 0.9557 Val PRC: 0.9557 Time: 0.23\n",
      "Epoch: 74 Train Loss: 0.2606 Acc: 0.8953 Pre: 0.9280 Recall: 0.8571 F1: 0.8911 Train AUC: 0.9571 Val AUC: 0.9574 Val PRC: 0.9561 Time: 0.23\n",
      "Epoch: 75 Train Loss: 0.2664 Acc: 0.8924 Pre: 0.9002 Recall: 0.8826 F1: 0.8913 Train AUC: 0.9565 Val AUC: 0.9546 Val PRC: 0.9495 Time: 0.23\n",
      "Epoch: 76 Train Loss: 0.2852 Acc: 0.8659 Pre: 0.8388 Recall: 0.9061 F1: 0.8711 Train AUC: 0.9499 Val AUC: 0.9492 Val PRC: 0.9531 Time: 0.23\n",
      "Epoch: 77 Train Loss: 0.2675 Acc: 0.8933 Pre: 0.8851 Recall: 0.9041 F1: 0.8945 Train AUC: 0.9551 Val AUC: 0.9554 Val PRC: 0.9518 Time: 0.24\n",
      "Epoch: 78 Train Loss: 0.2719 Acc: 0.8933 Pre: 0.8836 Recall: 0.9061 F1: 0.8947 Train AUC: 0.9539 Val AUC: 0.9544 Val PRC: 0.9486 Time: 0.24\n",
      "Epoch: 79 Train Loss: 0.2761 Acc: 0.8894 Pre: 0.9078 Recall: 0.8669 F1: 0.8869 Train AUC: 0.9530 Val AUC: 0.9553 Val PRC: 0.9582 Time: 0.23\n",
      "Epoch: 80 Train Loss: 0.2542 Acc: 0.8953 Pre: 0.8855 Recall: 0.9080 F1: 0.8966 Train AUC: 0.9613 Val AUC: 0.9583 Val PRC: 0.9592 Time: 0.23\n",
      "Epoch: 81 Train Loss: 0.2465 Acc: 0.8933 Pre: 0.9102 Recall: 0.8728 F1: 0.8911 Train AUC: 0.9615 Val AUC: 0.9584 Val PRC: 0.9620 Time: 0.25\n",
      "Epoch: 82 Train Loss: 0.2612 Acc: 0.8904 Pre: 0.8800 Recall: 0.9041 F1: 0.8919 Train AUC: 0.9576 Val AUC: 0.9586 Val PRC: 0.9625 Time: 0.25\n",
      "Epoch: 83 Train Loss: 0.2562 Acc: 0.8933 Pre: 0.8836 Recall: 0.9061 F1: 0.8947 Train AUC: 0.9585 Val AUC: 0.9568 Val PRC: 0.9605 Time: 0.23\n",
      "Epoch: 84 Train Loss: 0.2538 Acc: 0.8914 Pre: 0.8831 Recall: 0.9022 F1: 0.8925 Train AUC: 0.9600 Val AUC: 0.9598 Val PRC: 0.9560 Time: 0.23\n",
      "Epoch: 85 Train Loss: 0.2605 Acc: 0.8943 Pre: 0.9054 Recall: 0.8806 F1: 0.8929 Train AUC: 0.9585 Val AUC: 0.9594 Val PRC: 0.9615 Time: 0.23\n",
      "Epoch: 86 Train Loss: 0.2412 Acc: 0.8953 Pre: 0.8646 Recall: 0.9374 F1: 0.8995 Train AUC: 0.9641 Val AUC: 0.9613 Val PRC: 0.9580 Time: 0.23\n",
      "Epoch: 87 Train Loss: 0.2480 Acc: 0.8933 Pre: 0.9069 Recall: 0.8767 F1: 0.8915 Train AUC: 0.9620 Val AUC: 0.9606 Val PRC: 0.9623 Time: 0.24\n",
      "Epoch: 88 Train Loss: 0.2711 Acc: 0.8953 Pre: 0.8976 Recall: 0.8924 F1: 0.8950 Train AUC: 0.9558 Val AUC: 0.9588 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 89 Train Loss: 0.2547 Acc: 0.9012 Pre: 0.9051 Recall: 0.8963 F1: 0.9007 Train AUC: 0.9615 Val AUC: 0.9634 Val PRC: 0.9648 Time: 0.23\n",
      "Epoch: 90 Train Loss: 0.2491 Acc: 0.9002 Pre: 0.8698 Recall: 0.9413 F1: 0.9041 Train AUC: 0.9631 Val AUC: 0.9636 Val PRC: 0.9647 Time: 0.23\n",
      "Epoch: 91 Train Loss: 0.2488 Acc: 0.9022 Pre: 0.9053 Recall: 0.8982 F1: 0.9018 Train AUC: 0.9627 Val AUC: 0.9631 Val PRC: 0.9592 Time: 0.23\n",
      "Epoch: 92 Train Loss: 0.2399 Acc: 0.9041 Pre: 0.9206 Recall: 0.8845 F1: 0.9022 Train AUC: 0.9653 Val AUC: 0.9629 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 93 Train Loss: 0.2490 Acc: 0.8963 Pre: 0.8857 Recall: 0.9100 F1: 0.8977 Train AUC: 0.9629 Val AUC: 0.9636 Val PRC: 0.9591 Time: 0.23\n",
      "Epoch: 94 Train Loss: 0.2433 Acc: 0.8943 Pre: 0.8809 Recall: 0.9119 F1: 0.8962 Train AUC: 0.9641 Val AUC: 0.9598 Val PRC: 0.9549 Time: 0.24\n",
      "Epoch: 95 Train Loss: 0.2458 Acc: 0.8982 Pre: 0.8818 Recall: 0.9198 F1: 0.9004 Train AUC: 0.9628 Val AUC: 0.9613 Val PRC: 0.9639 Time: 0.23\n",
      "Epoch: 96 Train Loss: 0.2314 Acc: 0.9051 Pre: 0.8848 Recall: 0.9315 F1: 0.9075 Train AUC: 0.9680 Val AUC: 0.9644 Val PRC: 0.9637 Time: 0.23\n",
      "Epoch: 97 Train Loss: 0.2425 Acc: 0.9031 Pre: 0.8946 Recall: 0.9139 F1: 0.9042 Train AUC: 0.9645 Val AUC: 0.9646 Val PRC: 0.9610 Time: 0.23\n",
      "Epoch: 98 Train Loss: 0.2500 Acc: 0.9070 Pre: 0.9094 Recall: 0.9041 F1: 0.9068 Train AUC: 0.9617 Val AUC: 0.9630 Val PRC: 0.9593 Time: 0.24\n",
      "Epoch: 99 Train Loss: 0.2431 Acc: 0.9061 Pre: 0.9061 Recall: 0.9061 F1: 0.9061 Train AUC: 0.9640 Val AUC: 0.9646 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 100 Train Loss: 0.2439 Acc: 0.9031 Pre: 0.8962 Recall: 0.9119 F1: 0.9040 Train AUC: 0.9638 Val AUC: 0.9620 Val PRC: 0.9601 Time: 0.23\n",
      "Epoch: 101 Train Loss: 0.2277 Acc: 0.8973 Pre: 0.8651 Recall: 0.9413 F1: 0.9016 Train AUC: 0.9686 Val AUC: 0.9626 Val PRC: 0.9596 Time: 0.23\n",
      "Epoch: 102 Train Loss: 0.2344 Acc: 0.9080 Pre: 0.9017 Recall: 0.9159 F1: 0.9087 Train AUC: 0.9667 Val AUC: 0.9627 Val PRC: 0.9586 Time: 0.23\n",
      "Epoch: 103 Train Loss: 0.2351 Acc: 0.9080 Pre: 0.9129 Recall: 0.9022 F1: 0.9075 Train AUC: 0.9666 Val AUC: 0.9651 Val PRC: 0.9627 Time: 0.23\n",
      "Epoch: 104 Train Loss: 0.2480 Acc: 0.9012 Pre: 0.9150 Recall: 0.8845 F1: 0.8995 Train AUC: 0.9630 Val AUC: 0.9640 Val PRC: 0.9610 Time: 0.23\n",
      "Epoch: 105 Train Loss: 0.2408 Acc: 0.9031 Pre: 0.8902 Recall: 0.9198 F1: 0.9047 Train AUC: 0.9651 Val AUC: 0.9645 Val PRC: 0.9600 Time: 0.23\n",
      "Epoch: 106 Train Loss: 0.2294 Acc: 0.9061 Pre: 0.9014 Recall: 0.9119 F1: 0.9066 Train AUC: 0.9683 Val AUC: 0.9640 Val PRC: 0.9650 Time: 0.24\n",
      "Epoch: 107 Train Loss: 0.2301 Acc: 0.9022 Pre: 0.8771 Recall: 0.9354 F1: 0.9053 Train AUC: 0.9680 Val AUC: 0.9642 Val PRC: 0.9641 Time: 0.24\n",
      "Epoch: 108 Train Loss: 0.2556 Acc: 0.9119 Pre: 0.8979 Recall: 0.9295 F1: 0.9135 Train AUC: 0.9597 Val AUC: 0.9639 Val PRC: 0.9592 Time: 0.23\n",
      "Epoch: 109 Train Loss: 0.2268 Acc: 0.9090 Pre: 0.9019 Recall: 0.9178 F1: 0.9098 Train AUC: 0.9690 Val AUC: 0.9651 Val PRC: 0.9610 Time: 0.23\n",
      "Epoch: 110 Train Loss: 0.2319 Acc: 0.9061 Pre: 0.8967 Recall: 0.9178 F1: 0.9072 Train AUC: 0.9675 Val AUC: 0.9600 Val PRC: 0.9548 Time: 0.23\n",
      "Epoch: 111 Train Loss: 0.2219 Acc: 0.9061 Pre: 0.8835 Recall: 0.9354 F1: 0.9087 Train AUC: 0.9701 Val AUC: 0.9650 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 112 Train Loss: 0.2342 Acc: 0.9139 Pre: 0.9325 Recall: 0.8924 F1: 0.9120 Train AUC: 0.9668 Val AUC: 0.9659 Val PRC: 0.9651 Time: 0.23\n",
      "Epoch: 113 Train Loss: 0.2284 Acc: 0.9139 Pre: 0.8983 Recall: 0.9335 F1: 0.9155 Train AUC: 0.9685 Val AUC: 0.9660 Val PRC: 0.9634 Time: 0.23\n",
      "Epoch: 114 Train Loss: 0.2312 Acc: 0.9149 Pre: 0.9157 Recall: 0.9139 F1: 0.9148 Train AUC: 0.9674 Val AUC: 0.9647 Val PRC: 0.9572 Time: 0.23\n",
      "Epoch: 115 Train Loss: 0.2255 Acc: 0.9110 Pre: 0.9150 Recall: 0.9061 F1: 0.9105 Train AUC: 0.9698 Val AUC: 0.9661 Val PRC: 0.9648 Time: 0.23\n",
      "Epoch: 116 Train Loss: 0.2262 Acc: 0.9090 Pre: 0.9197 Recall: 0.8963 F1: 0.9078 Train AUC: 0.9695 Val AUC: 0.9678 Val PRC: 0.9688 Time: 0.24\n",
      "Epoch: 117 Train Loss: 0.2226 Acc: 0.9110 Pre: 0.8933 Recall: 0.9335 F1: 0.9129 Train AUC: 0.9698 Val AUC: 0.9686 Val PRC: 0.9662 Time: 0.23\n",
      "Epoch: 118 Train Loss: 0.2288 Acc: 0.9080 Pre: 0.8883 Recall: 0.9335 F1: 0.9103 Train AUC: 0.9686 Val AUC: 0.9661 Val PRC: 0.9646 Time: 0.23\n",
      "Epoch: 119 Train Loss: 0.2159 Acc: 0.9139 Pre: 0.9029 Recall: 0.9276 F1: 0.9151 Train AUC: 0.9720 Val AUC: 0.9687 Val PRC: 0.9658 Time: 0.24\n",
      "Epoch: 120 Train Loss: 0.2227 Acc: 0.9070 Pre: 0.9078 Recall: 0.9061 F1: 0.9070 Train AUC: 0.9696 Val AUC: 0.9637 Val PRC: 0.9563 Time: 0.23\n",
      "Epoch: 121 Train Loss: 0.2222 Acc: 0.9149 Pre: 0.8926 Recall: 0.9432 F1: 0.9172 Train AUC: 0.9700 Val AUC: 0.9678 Val PRC: 0.9634 Time: 0.23\n",
      "Epoch: 122 Train Loss: 0.2224 Acc: 0.9129 Pre: 0.8981 Recall: 0.9315 F1: 0.9145 Train AUC: 0.9695 Val AUC: 0.9665 Val PRC: 0.9629 Time: 0.23\n",
      "Epoch: 123 Train Loss: 0.2203 Acc: 0.9119 Pre: 0.9010 Recall: 0.9256 F1: 0.9131 Train AUC: 0.9700 Val AUC: 0.9660 Val PRC: 0.9631 Time: 0.24\n",
      "Epoch: 124 Train Loss: 0.2195 Acc: 0.9139 Pre: 0.9273 Recall: 0.8982 F1: 0.9125 Train AUC: 0.9709 Val AUC: 0.9664 Val PRC: 0.9656 Time: 0.23\n",
      "Epoch: 125 Train Loss: 0.2235 Acc: 0.9110 Pre: 0.9008 Recall: 0.9237 F1: 0.9121 Train AUC: 0.9696 Val AUC: 0.9662 Val PRC: 0.9621 Time: 0.27\n",
      "Epoch: 126 Train Loss: 0.2103 Acc: 0.9139 Pre: 0.9013 Recall: 0.9295 F1: 0.9152 Train AUC: 0.9735 Val AUC: 0.9674 Val PRC: 0.9646 Time: 0.24\n",
      "Epoch: 127 Train Loss: 0.2057 Acc: 0.9168 Pre: 0.9128 Recall: 0.9217 F1: 0.9172 Train AUC: 0.9742 Val AUC: 0.9652 Val PRC: 0.9592 Time: 0.24\n",
      "Epoch: 128 Train Loss: 0.2227 Acc: 0.9149 Pre: 0.9141 Recall: 0.9159 F1: 0.9150 Train AUC: 0.9697 Val AUC: 0.9647 Val PRC: 0.9588 Time: 0.23\n",
      "Epoch: 129 Train Loss: 0.2188 Acc: 0.9159 Pre: 0.9048 Recall: 0.9295 F1: 0.9170 Train AUC: 0.9709 Val AUC: 0.9672 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 130 Train Loss: 0.2052 Acc: 0.9149 Pre: 0.8941 Recall: 0.9413 F1: 0.9171 Train AUC: 0.9751 Val AUC: 0.9660 Val PRC: 0.9645 Time: 0.23\n",
      "Epoch: 131 Train Loss: 0.2154 Acc: 0.9149 Pre: 0.9030 Recall: 0.9295 F1: 0.9161 Train AUC: 0.9718 Val AUC: 0.9671 Val PRC: 0.9632 Time: 0.42\n",
      "Epoch: 132 Train Loss: 0.2051 Acc: 0.9149 Pre: 0.9190 Recall: 0.9100 F1: 0.9145 Train AUC: 0.9747 Val AUC: 0.9670 Val PRC: 0.9649 Time: 0.23\n",
      "Epoch: 133 Train Loss: 0.2109 Acc: 0.9129 Pre: 0.9154 Recall: 0.9100 F1: 0.9127 Train AUC: 0.9725 Val AUC: 0.9640 Val PRC: 0.9589 Time: 0.24\n",
      "Epoch: 134 Train Loss: 0.2143 Acc: 0.9129 Pre: 0.9154 Recall: 0.9100 F1: 0.9127 Train AUC: 0.9723 Val AUC: 0.9679 Val PRC: 0.9649 Time: 0.24\n",
      "Epoch: 135 Train Loss: 0.2218 Acc: 0.9207 Pre: 0.9335 Recall: 0.9061 F1: 0.9196 Train AUC: 0.9700 Val AUC: 0.9685 Val PRC: 0.9658 Time: 0.24\n",
      "Epoch: 136 Train Loss: 0.2027 Acc: 0.9119 Pre: 0.8979 Recall: 0.9295 F1: 0.9135 Train AUC: 0.9754 Val AUC: 0.9687 Val PRC: 0.9671 Time: 0.24\n",
      "Epoch: 137 Train Loss: 0.2100 Acc: 0.9188 Pre: 0.9163 Recall: 0.9217 F1: 0.9190 Train AUC: 0.9731 Val AUC: 0.9707 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 138 Train Loss: 0.2034 Acc: 0.9070 Pre: 0.8852 Recall: 0.9354 F1: 0.9096 Train AUC: 0.9749 Val AUC: 0.9668 Val PRC: 0.9636 Time: 0.23\n",
      "Epoch: 139 Train Loss: 0.1985 Acc: 0.9110 Pre: 0.8992 Recall: 0.9256 F1: 0.9122 Train AUC: 0.9762 Val AUC: 0.9684 Val PRC: 0.9651 Time: 0.23\n",
      "Epoch: 140 Train Loss: 0.2061 Acc: 0.9119 Pre: 0.9056 Recall: 0.9198 F1: 0.9126 Train AUC: 0.9739 Val AUC: 0.9668 Val PRC: 0.9670 Time: 0.24\n",
      "Epoch: 141 Train Loss: 0.2247 Acc: 0.9159 Pre: 0.9142 Recall: 0.9178 F1: 0.9160 Train AUC: 0.9688 Val AUC: 0.9686 Val PRC: 0.9591 Time: 0.24\n",
      "Epoch: 142 Train Loss: 0.2040 Acc: 0.9149 Pre: 0.9061 Recall: 0.9256 F1: 0.9158 Train AUC: 0.9753 Val AUC: 0.9687 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 143 Train Loss: 0.2016 Acc: 0.9129 Pre: 0.8996 Recall: 0.9295 F1: 0.9143 Train AUC: 0.9754 Val AUC: 0.9688 Val PRC: 0.9673 Time: 0.24\n",
      "Epoch: 144 Train Loss: 0.1982 Acc: 0.9149 Pre: 0.9030 Recall: 0.9295 F1: 0.9161 Train AUC: 0.9760 Val AUC: 0.9675 Val PRC: 0.9678 Time: 0.23\n",
      "Epoch: 145 Train Loss: 0.2042 Acc: 0.9119 Pre: 0.8807 Recall: 0.9530 F1: 0.9154 Train AUC: 0.9742 Val AUC: 0.9678 Val PRC: 0.9654 Time: 0.23\n",
      "Epoch: 146 Train Loss: 0.2051 Acc: 0.9168 Pre: 0.9209 Recall: 0.9119 F1: 0.9164 Train AUC: 0.9736 Val AUC: 0.9703 Val PRC: 0.9641 Time: 0.23\n",
      "Epoch: 147 Train Loss: 0.2003 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9755 Val AUC: 0.9725 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 148 Train Loss: 0.1984 Acc: 0.9188 Pre: 0.9131 Recall: 0.9256 F1: 0.9193 Train AUC: 0.9769 Val AUC: 0.9713 Val PRC: 0.9709 Time: 0.24\n",
      "Epoch: 149 Train Loss: 0.2029 Acc: 0.9149 Pre: 0.9157 Recall: 0.9139 F1: 0.9148 Train AUC: 0.9746 Val AUC: 0.9703 Val PRC: 0.9686 Time: 0.30\n",
      "Epoch: 150 Train Loss: 0.1972 Acc: 0.9139 Pre: 0.8939 Recall: 0.9393 F1: 0.9160 Train AUC: 0.9765 Val AUC: 0.9678 Val PRC: 0.9663 Time: 0.29\n",
      "Epoch: 151 Train Loss: 0.2017 Acc: 0.9149 Pre: 0.9157 Recall: 0.9139 F1: 0.9148 Train AUC: 0.9755 Val AUC: 0.9701 Val PRC: 0.9697 Time: 0.24\n",
      "Epoch: 152 Train Loss: 0.1962 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9765 Val AUC: 0.9718 Val PRC: 0.9678 Time: 0.23\n",
      "Epoch: 153 Train Loss: 0.2081 Acc: 0.9178 Pre: 0.9194 Recall: 0.9159 F1: 0.9176 Train AUC: 0.9742 Val AUC: 0.9707 Val PRC: 0.9693 Time: 0.23\n",
      "Epoch: 154 Train Loss: 0.2015 Acc: 0.9188 Pre: 0.9263 Recall: 0.9100 F1: 0.9181 Train AUC: 0.9748 Val AUC: 0.9698 Val PRC: 0.9708 Time: 0.24\n",
      "Epoch: 155 Train Loss: 0.1870 Acc: 0.9207 Pre: 0.9232 Recall: 0.9178 F1: 0.9205 Train AUC: 0.9789 Val AUC: 0.9703 Val PRC: 0.9685 Time: 0.24\n",
      "Epoch: 156 Train Loss: 0.2022 Acc: 0.9159 Pre: 0.8987 Recall: 0.9374 F1: 0.9176 Train AUC: 0.9752 Val AUC: 0.9713 Val PRC: 0.9705 Time: 0.23\n",
      "Epoch: 157 Train Loss: 0.1999 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9752 Val AUC: 0.9686 Val PRC: 0.9624 Time: 0.24\n",
      "Epoch: 158 Train Loss: 0.1907 Acc: 0.9237 Pre: 0.9108 Recall: 0.9393 F1: 0.9249 Train AUC: 0.9782 Val AUC: 0.9715 Val PRC: 0.9676 Time: 0.23\n",
      "Epoch: 159 Train Loss: 0.2066 Acc: 0.9159 Pre: 0.9159 Recall: 0.9159 F1: 0.9159 Train AUC: 0.9754 Val AUC: 0.9682 Val PRC: 0.9672 Time: 0.24\n",
      "Epoch: 160 Train Loss: 0.1920 Acc: 0.9159 Pre: 0.9048 Recall: 0.9295 F1: 0.9170 Train AUC: 0.9765 Val AUC: 0.9707 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 161 Train Loss: 0.1952 Acc: 0.9188 Pre: 0.9068 Recall: 0.9335 F1: 0.9200 Train AUC: 0.9759 Val AUC: 0.9692 Val PRC: 0.9684 Time: 0.24\n",
      "Epoch: 162 Train Loss: 0.2024 Acc: 0.9110 Pre: 0.8860 Recall: 0.9432 F1: 0.9137 Train AUC: 0.9749 Val AUC: 0.9721 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 163 Train Loss: 0.1926 Acc: 0.9178 Pre: 0.9021 Recall: 0.9374 F1: 0.9194 Train AUC: 0.9765 Val AUC: 0.9724 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 164 Train Loss: 0.2023 Acc: 0.9198 Pre: 0.9214 Recall: 0.9178 F1: 0.9196 Train AUC: 0.9749 Val AUC: 0.9723 Val PRC: 0.9719 Time: 0.23\n",
      "Epoch: 165 Train Loss: 0.1973 Acc: 0.9237 Pre: 0.9188 Recall: 0.9295 F1: 0.9241 Train AUC: 0.9764 Val AUC: 0.9719 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 166 Train Loss: 0.1949 Acc: 0.9188 Pre: 0.9246 Recall: 0.9119 F1: 0.9182 Train AUC: 0.9760 Val AUC: 0.9690 Val PRC: 0.9693 Time: 0.23\n",
      "Epoch: 167 Train Loss: 0.1889 Acc: 0.9119 Pre: 0.8834 Recall: 0.9491 F1: 0.9151 Train AUC: 0.9777 Val AUC: 0.9720 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 168 Train Loss: 0.1904 Acc: 0.9217 Pre: 0.9168 Recall: 0.9276 F1: 0.9222 Train AUC: 0.9770 Val AUC: 0.9718 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 169 Train Loss: 0.1914 Acc: 0.9149 Pre: 0.9109 Recall: 0.9198 F1: 0.9153 Train AUC: 0.9769 Val AUC: 0.9710 Val PRC: 0.9715 Time: 0.23\n",
      "Epoch: 170 Train Loss: 0.1936 Acc: 0.9227 Pre: 0.9170 Recall: 0.9295 F1: 0.9232 Train AUC: 0.9761 Val AUC: 0.9705 Val PRC: 0.9708 Time: 0.24\n",
      "Epoch: 171 Train Loss: 0.1833 Acc: 0.9256 Pre: 0.9143 Recall: 0.9393 F1: 0.9266 Train AUC: 0.9793 Val AUC: 0.9707 Val PRC: 0.9687 Time: 0.24\n",
      "Epoch: 172 Train Loss: 0.1909 Acc: 0.9217 Pre: 0.9028 Recall: 0.9452 F1: 0.9235 Train AUC: 0.9767 Val AUC: 0.9723 Val PRC: 0.9636 Time: 0.23\n",
      "Epoch: 173 Train Loss: 0.1858 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9787 Val AUC: 0.9718 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 174 Train Loss: 0.1918 Acc: 0.9198 Pre: 0.9101 Recall: 0.9315 F1: 0.9207 Train AUC: 0.9766 Val AUC: 0.9675 Val PRC: 0.9591 Time: 0.23\n",
      "Epoch: 175 Train Loss: 0.1803 Acc: 0.9178 Pre: 0.9082 Recall: 0.9295 F1: 0.9188 Train AUC: 0.9800 Val AUC: 0.9699 Val PRC: 0.9685 Time: 0.23\n",
      "Epoch: 176 Train Loss: 0.1812 Acc: 0.9188 Pre: 0.9180 Recall: 0.9198 F1: 0.9189 Train AUC: 0.9793 Val AUC: 0.9702 Val PRC: 0.9684 Time: 0.24\n",
      "Epoch: 177 Train Loss: 0.1889 Acc: 0.9188 Pre: 0.9115 Recall: 0.9276 F1: 0.9195 Train AUC: 0.9777 Val AUC: 0.9726 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 178 Train Loss: 0.1839 Acc: 0.9198 Pre: 0.9009 Recall: 0.9432 F1: 0.9216 Train AUC: 0.9790 Val AUC: 0.9726 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 179 Train Loss: 0.2106 Acc: 0.9295 Pre: 0.9312 Recall: 0.9276 F1: 0.9294 Train AUC: 0.9777 Val AUC: 0.9732 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 180 Train Loss: 0.1840 Acc: 0.9159 Pre: 0.8957 Recall: 0.9413 F1: 0.9179 Train AUC: 0.9790 Val AUC: 0.9709 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 181 Train Loss: 0.1800 Acc: 0.9256 Pre: 0.9175 Recall: 0.9354 F1: 0.9264 Train AUC: 0.9800 Val AUC: 0.9736 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 182 Train Loss: 0.1801 Acc: 0.9207 Pre: 0.9183 Recall: 0.9237 F1: 0.9210 Train AUC: 0.9798 Val AUC: 0.9716 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 183 Train Loss: 0.1769 Acc: 0.9247 Pre: 0.9189 Recall: 0.9315 F1: 0.9252 Train AUC: 0.9804 Val AUC: 0.9720 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 184 Train Loss: 0.1637 Acc: 0.9227 Pre: 0.9444 Recall: 0.8982 F1: 0.9208 Train AUC: 0.9831 Val AUC: 0.9718 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 185 Train Loss: 0.1835 Acc: 0.9217 Pre: 0.9234 Recall: 0.9198 F1: 0.9216 Train AUC: 0.9780 Val AUC: 0.9722 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 186 Train Loss: 0.1773 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9798 Val AUC: 0.9700 Val PRC: 0.9664 Time: 0.24\n",
      "Epoch: 187 Train Loss: 0.1775 Acc: 0.9217 Pre: 0.8983 Recall: 0.9511 F1: 0.9240 Train AUC: 0.9797 Val AUC: 0.9707 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 188 Train Loss: 0.1764 Acc: 0.9178 Pre: 0.8875 Recall: 0.9569 F1: 0.9209 Train AUC: 0.9798 Val AUC: 0.9730 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 189 Train Loss: 0.1758 Acc: 0.9207 Pre: 0.8996 Recall: 0.9472 F1: 0.9228 Train AUC: 0.9797 Val AUC: 0.9734 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 190 Train Loss: 0.1748 Acc: 0.9247 Pre: 0.9222 Recall: 0.9276 F1: 0.9249 Train AUC: 0.9803 Val AUC: 0.9725 Val PRC: 0.9747 Time: 0.24\n",
      "Epoch: 191 Train Loss: 0.1755 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9813 Val AUC: 0.9724 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 192 Train Loss: 0.1726 Acc: 0.9217 Pre: 0.9089 Recall: 0.9374 F1: 0.9229 Train AUC: 0.9813 Val AUC: 0.9730 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 193 Train Loss: 0.1713 Acc: 0.9207 Pre: 0.9041 Recall: 0.9413 F1: 0.9223 Train AUC: 0.9820 Val AUC: 0.9727 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 194 Train Loss: 0.1647 Acc: 0.9247 Pre: 0.9222 Recall: 0.9276 F1: 0.9249 Train AUC: 0.9824 Val AUC: 0.9733 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 195 Train Loss: 0.1889 Acc: 0.9237 Pre: 0.9062 Recall: 0.9452 F1: 0.9253 Train AUC: 0.9765 Val AUC: 0.9701 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 196 Train Loss: 0.1800 Acc: 0.9217 Pre: 0.9336 Recall: 0.9080 F1: 0.9206 Train AUC: 0.9790 Val AUC: 0.9702 Val PRC: 0.9644 Time: 0.23\n",
      "Epoch: 197 Train Loss: 0.1675 Acc: 0.9217 Pre: 0.8969 Recall: 0.9530 F1: 0.9241 Train AUC: 0.9811 Val AUC: 0.9682 Val PRC: 0.9699 Time: 0.23\n",
      "Epoch: 198 Train Loss: 0.1753 Acc: 0.9247 Pre: 0.9094 Recall: 0.9432 F1: 0.9260 Train AUC: 0.9801 Val AUC: 0.9701 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 199 Train Loss: 0.1741 Acc: 0.9198 Pre: 0.8994 Recall: 0.9452 F1: 0.9218 Train AUC: 0.9812 Val AUC: 0.9698 Val PRC: 0.9626 Time: 0.23\n",
      "Epoch: 200 Train Loss: 0.1703 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9814 Val AUC: 0.9722 Val PRC: 0.9726 Time: 0.23\n",
      "Epoch: 201 Train Loss: 0.1589 Acc: 0.9325 Pre: 0.9139 Recall: 0.9550 F1: 0.9340 Train AUC: 0.9841 Val AUC: 0.9747 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 202 Train Loss: 0.1702 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9812 Val AUC: 0.9736 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 203 Train Loss: 0.1746 Acc: 0.9149 Pre: 0.8911 Recall: 0.9452 F1: 0.9174 Train AUC: 0.9806 Val AUC: 0.9699 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 204 Train Loss: 0.1750 Acc: 0.9266 Pre: 0.9225 Recall: 0.9315 F1: 0.9270 Train AUC: 0.9805 Val AUC: 0.9726 Val PRC: 0.9735 Time: 0.23\n",
      "Epoch: 205 Train Loss: 0.1757 Acc: 0.9207 Pre: 0.9352 Recall: 0.9041 F1: 0.9194 Train AUC: 0.9807 Val AUC: 0.9707 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 206 Train Loss: 0.1697 Acc: 0.9198 Pre: 0.9024 Recall: 0.9413 F1: 0.9215 Train AUC: 0.9813 Val AUC: 0.9706 Val PRC: 0.9647 Time: 0.23\n",
      "Epoch: 207 Train Loss: 0.1568 Acc: 0.9207 Pre: 0.8952 Recall: 0.9530 F1: 0.9232 Train AUC: 0.9841 Val AUC: 0.9726 Val PRC: 0.9710 Time: 0.23\n",
      "Epoch: 208 Train Loss: 0.1575 Acc: 0.9198 Pre: 0.8893 Recall: 0.9589 F1: 0.9228 Train AUC: 0.9841 Val AUC: 0.9703 Val PRC: 0.9712 Time: 0.24\n",
      "Epoch: 209 Train Loss: 0.1652 Acc: 0.9168 Pre: 0.8989 Recall: 0.9393 F1: 0.9187 Train AUC: 0.9822 Val AUC: 0.9739 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 210 Train Loss: 0.1562 Acc: 0.9188 Pre: 0.9084 Recall: 0.9315 F1: 0.9198 Train AUC: 0.9840 Val AUC: 0.9731 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 211 Train Loss: 0.1510 Acc: 0.9198 Pre: 0.8994 Recall: 0.9452 F1: 0.9218 Train AUC: 0.9855 Val AUC: 0.9721 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 212 Train Loss: 0.1607 Acc: 0.9217 Pre: 0.9105 Recall: 0.9354 F1: 0.9228 Train AUC: 0.9836 Val AUC: 0.9726 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 213 Train Loss: 0.1558 Acc: 0.9256 Pre: 0.9240 Recall: 0.9276 F1: 0.9258 Train AUC: 0.9840 Val AUC: 0.9713 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 214 Train Loss: 0.1751 Acc: 0.9247 Pre: 0.9079 Recall: 0.9452 F1: 0.9262 Train AUC: 0.9817 Val AUC: 0.9737 Val PRC: 0.9754 Time: 0.23\n",
      "Epoch: 215 Train Loss: 0.1563 Acc: 0.9276 Pre: 0.9243 Recall: 0.9315 F1: 0.9279 Train AUC: 0.9837 Val AUC: 0.9723 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 216 Train Loss: 0.1576 Acc: 0.9227 Pre: 0.9045 Recall: 0.9452 F1: 0.9244 Train AUC: 0.9839 Val AUC: 0.9707 Val PRC: 0.9714 Time: 0.23\n",
      "Epoch: 217 Train Loss: 0.1618 Acc: 0.9227 Pre: 0.9060 Recall: 0.9432 F1: 0.9243 Train AUC: 0.9834 Val AUC: 0.9734 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 218 Train Loss: 0.1762 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9801 Val AUC: 0.9722 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 219 Train Loss: 0.1557 Acc: 0.9227 Pre: 0.9138 Recall: 0.9335 F1: 0.9235 Train AUC: 0.9849 Val AUC: 0.9710 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 220 Train Loss: 0.1818 Acc: 0.9237 Pre: 0.9093 Recall: 0.9413 F1: 0.9250 Train AUC: 0.9835 Val AUC: 0.9716 Val PRC: 0.9726 Time: 0.24\n",
      "Epoch: 221 Train Loss: 0.1649 Acc: 0.9256 Pre: 0.9127 Recall: 0.9413 F1: 0.9268 Train AUC: 0.9828 Val AUC: 0.9708 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 222 Train Loss: 0.1519 Acc: 0.9207 Pre: 0.8967 Recall: 0.9511 F1: 0.9231 Train AUC: 0.9848 Val AUC: 0.9722 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 223 Train Loss: 0.1586 Acc: 0.9198 Pre: 0.9198 Recall: 0.9198 F1: 0.9198 Train AUC: 0.9839 Val AUC: 0.9732 Val PRC: 0.9735 Time: 0.23\n",
      "Epoch: 224 Train Loss: 0.1559 Acc: 0.9217 Pre: 0.9074 Recall: 0.9393 F1: 0.9231 Train AUC: 0.9839 Val AUC: 0.9731 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 225 Train Loss: 0.1547 Acc: 0.9247 Pre: 0.9033 Recall: 0.9511 F1: 0.9266 Train AUC: 0.9840 Val AUC: 0.9724 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 226 Train Loss: 0.1580 Acc: 0.9237 Pre: 0.9155 Recall: 0.9335 F1: 0.9244 Train AUC: 0.9832 Val AUC: 0.9708 Val PRC: 0.9696 Time: 0.24\n",
      "Epoch: 227 Train Loss: 0.1511 Acc: 0.9266 Pre: 0.9129 Recall: 0.9432 F1: 0.9278 Train AUC: 0.9844 Val AUC: 0.9739 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 228 Train Loss: 0.1438 Acc: 0.9315 Pre: 0.9298 Recall: 0.9335 F1: 0.9316 Train AUC: 0.9870 Val AUC: 0.9748 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 229 Train Loss: 0.1525 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9844 Val AUC: 0.9726 Val PRC: 0.9728 Time: 0.24\n",
      "Epoch: 230 Train Loss: 0.1557 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9850 Val AUC: 0.9724 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 231 Train Loss: 0.1596 Acc: 0.9266 Pre: 0.9192 Recall: 0.9354 F1: 0.9273 Train AUC: 0.9832 Val AUC: 0.9735 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 232 Train Loss: 0.1632 Acc: 0.9227 Pre: 0.9060 Recall: 0.9432 F1: 0.9243 Train AUC: 0.9814 Val AUC: 0.9733 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 233 Train Loss: 0.1535 Acc: 0.9237 Pre: 0.9321 Recall: 0.9139 F1: 0.9229 Train AUC: 0.9843 Val AUC: 0.9700 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 234 Train Loss: 0.1627 Acc: 0.9188 Pre: 0.8978 Recall: 0.9452 F1: 0.9209 Train AUC: 0.9823 Val AUC: 0.9719 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 235 Train Loss: 0.1603 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9838 Val AUC: 0.9720 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 236 Train Loss: 0.1461 Acc: 0.9247 Pre: 0.9222 Recall: 0.9276 F1: 0.9249 Train AUC: 0.9848 Val AUC: 0.9739 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 237 Train Loss: 0.1476 Acc: 0.9295 Pre: 0.9295 Recall: 0.9295 F1: 0.9295 Train AUC: 0.9856 Val AUC: 0.9745 Val PRC: 0.9767 Time: 0.24\n",
      "Epoch: 238 Train Loss: 0.1565 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9829 Val AUC: 0.9718 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 239 Train Loss: 0.1760 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9848 Val AUC: 0.9698 Val PRC: 0.9706 Time: 0.24\n",
      "Epoch: 240 Train Loss: 0.1451 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9860 Val AUC: 0.9694 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 241 Train Loss: 0.1473 Acc: 0.9247 Pre: 0.9238 Recall: 0.9256 F1: 0.9247 Train AUC: 0.9851 Val AUC: 0.9735 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 242 Train Loss: 0.1541 Acc: 0.9188 Pre: 0.8948 Recall: 0.9491 F1: 0.9212 Train AUC: 0.9835 Val AUC: 0.9703 Val PRC: 0.9702 Time: 0.23\n",
      "Epoch: 243 Train Loss: 0.1409 Acc: 0.9227 Pre: 0.9091 Recall: 0.9393 F1: 0.9240 Train AUC: 0.9861 Val AUC: 0.9702 Val PRC: 0.9665 Time: 0.23\n",
      "Epoch: 244 Train Loss: 0.1488 Acc: 0.9295 Pre: 0.9262 Recall: 0.9335 F1: 0.9298 Train AUC: 0.9840 Val AUC: 0.9739 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 245 Train Loss: 0.1493 Acc: 0.9256 Pre: 0.9096 Recall: 0.9452 F1: 0.9271 Train AUC: 0.9837 Val AUC: 0.9744 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 246 Train Loss: 0.1320 Acc: 0.9266 Pre: 0.9082 Recall: 0.9491 F1: 0.9282 Train AUC: 0.9876 Val AUC: 0.9695 Val PRC: 0.9645 Time: 0.23\n",
      "Epoch: 247 Train Loss: 0.1446 Acc: 0.9237 Pre: 0.9140 Recall: 0.9354 F1: 0.9246 Train AUC: 0.9853 Val AUC: 0.9740 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 248 Train Loss: 0.1581 Acc: 0.9227 Pre: 0.9122 Recall: 0.9354 F1: 0.9237 Train AUC: 0.9818 Val AUC: 0.9737 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 249 Train Loss: 0.1344 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9876 Val AUC: 0.9730 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 250 Train Loss: 0.1373 Acc: 0.9315 Pre: 0.9265 Recall: 0.9374 F1: 0.9319 Train AUC: 0.9869 Val AUC: 0.9754 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 251 Train Loss: 0.1511 Acc: 0.9295 Pre: 0.9134 Recall: 0.9491 F1: 0.9309 Train AUC: 0.9845 Val AUC: 0.9736 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 252 Train Loss: 0.1422 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9865 Val AUC: 0.9745 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 253 Train Loss: 0.1357 Acc: 0.9266 Pre: 0.9192 Recall: 0.9354 F1: 0.9273 Train AUC: 0.9871 Val AUC: 0.9729 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 254 Train Loss: 0.1451 Acc: 0.9227 Pre: 0.9091 Recall: 0.9393 F1: 0.9240 Train AUC: 0.9849 Val AUC: 0.9731 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 255 Train Loss: 0.1442 Acc: 0.9256 Pre: 0.9240 Recall: 0.9276 F1: 0.9258 Train AUC: 0.9852 Val AUC: 0.9735 Val PRC: 0.9739 Time: 0.24\n",
      "Epoch: 256 Train Loss: 0.1371 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9871 Val AUC: 0.9732 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 257 Train Loss: 0.1303 Acc: 0.9256 Pre: 0.9112 Recall: 0.9432 F1: 0.9269 Train AUC: 0.9881 Val AUC: 0.9774 Val PRC: 0.9793 Time: 0.23\n",
      "Epoch: 258 Train Loss: 0.1435 Acc: 0.9344 Pre: 0.9370 Recall: 0.9315 F1: 0.9342 Train AUC: 0.9853 Val AUC: 0.9766 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 259 Train Loss: 0.1344 Acc: 0.9276 Pre: 0.9259 Recall: 0.9295 F1: 0.9277 Train AUC: 0.9865 Val AUC: 0.9737 Val PRC: 0.9715 Time: 0.23\n",
      "Epoch: 260 Train Loss: 0.1438 Acc: 0.9266 Pre: 0.9098 Recall: 0.9472 F1: 0.9281 Train AUC: 0.9856 Val AUC: 0.9760 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 261 Train Loss: 0.1378 Acc: 0.9237 Pre: 0.9032 Recall: 0.9491 F1: 0.9256 Train AUC: 0.9866 Val AUC: 0.9726 Val PRC: 0.9718 Time: 0.24\n",
      "Epoch: 262 Train Loss: 0.1438 Acc: 0.9266 Pre: 0.9308 Recall: 0.9217 F1: 0.9263 Train AUC: 0.9851 Val AUC: 0.9749 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 263 Train Loss: 0.1391 Acc: 0.9276 Pre: 0.9243 Recall: 0.9315 F1: 0.9279 Train AUC: 0.9858 Val AUC: 0.9755 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 264 Train Loss: 0.1411 Acc: 0.9237 Pre: 0.9188 Recall: 0.9295 F1: 0.9241 Train AUC: 0.9849 Val AUC: 0.9721 Val PRC: 0.9699 Time: 0.42\n",
      "Epoch: 265 Train Loss: 0.1460 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9848 Val AUC: 0.9752 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 266 Train Loss: 0.1423 Acc: 0.9198 Pre: 0.9040 Recall: 0.9393 F1: 0.9213 Train AUC: 0.9855 Val AUC: 0.9750 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 267 Train Loss: 0.1411 Acc: 0.9237 Pre: 0.9108 Recall: 0.9393 F1: 0.9249 Train AUC: 0.9859 Val AUC: 0.9744 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 268 Train Loss: 0.1559 Acc: 0.9217 Pre: 0.9267 Recall: 0.9159 F1: 0.9213 Train AUC: 0.9829 Val AUC: 0.9718 Val PRC: 0.9716 Time: 0.24\n",
      "Epoch: 269 Train Loss: 0.1201 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9903 Val AUC: 0.9756 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 270 Train Loss: 0.1389 Acc: 0.9256 Pre: 0.9096 Recall: 0.9452 F1: 0.9271 Train AUC: 0.9862 Val AUC: 0.9764 Val PRC: 0.9768 Time: 0.23\n",
      "Epoch: 271 Train Loss: 0.1393 Acc: 0.9315 Pre: 0.9232 Recall: 0.9413 F1: 0.9322 Train AUC: 0.9864 Val AUC: 0.9759 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 272 Train Loss: 0.1397 Acc: 0.9305 Pre: 0.9400 Recall: 0.9198 F1: 0.9298 Train AUC: 0.9862 Val AUC: 0.9758 Val PRC: 0.9774 Time: 0.24\n",
      "Epoch: 273 Train Loss: 0.1369 Acc: 0.9295 Pre: 0.9165 Recall: 0.9452 F1: 0.9306 Train AUC: 0.9865 Val AUC: 0.9754 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 274 Train Loss: 0.1157 Acc: 0.9344 Pre: 0.9302 Recall: 0.9393 F1: 0.9348 Train AUC: 0.9905 Val AUC: 0.9752 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 275 Train Loss: 0.1267 Acc: 0.9217 Pre: 0.9043 Recall: 0.9432 F1: 0.9234 Train AUC: 0.9887 Val AUC: 0.9749 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 276 Train Loss: 0.1325 Acc: 0.9344 Pre: 0.9422 Recall: 0.9256 F1: 0.9339 Train AUC: 0.9874 Val AUC: 0.9750 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 277 Train Loss: 0.1354 Acc: 0.9325 Pre: 0.9402 Recall: 0.9237 F1: 0.9319 Train AUC: 0.9864 Val AUC: 0.9747 Val PRC: 0.9753 Time: 0.24\n",
      "Epoch: 278 Train Loss: 0.1303 Acc: 0.9207 Pre: 0.9057 Recall: 0.9393 F1: 0.9222 Train AUC: 0.9875 Val AUC: 0.9707 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 279 Train Loss: 0.1355 Acc: 0.9305 Pre: 0.9183 Recall: 0.9452 F1: 0.9315 Train AUC: 0.9860 Val AUC: 0.9746 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 280 Train Loss: 0.1404 Acc: 0.9276 Pre: 0.9210 Recall: 0.9354 F1: 0.9282 Train AUC: 0.9840 Val AUC: 0.9755 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 281 Train Loss: 0.1513 Acc: 0.9315 Pre: 0.9282 Recall: 0.9354 F1: 0.9318 Train AUC: 0.9829 Val AUC: 0.9738 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 282 Train Loss: 0.1358 Acc: 0.9364 Pre: 0.9305 Recall: 0.9432 F1: 0.9368 Train AUC: 0.9865 Val AUC: 0.9744 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 283 Train Loss: 0.1261 Acc: 0.9354 Pre: 0.9354 Recall: 0.9354 F1: 0.9354 Train AUC: 0.9889 Val AUC: 0.9765 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 284 Train Loss: 0.1315 Acc: 0.9364 Pre: 0.9288 Recall: 0.9452 F1: 0.9370 Train AUC: 0.9872 Val AUC: 0.9768 Val PRC: 0.9767 Time: 0.24\n",
      "Epoch: 285 Train Loss: 0.1349 Acc: 0.9266 Pre: 0.9241 Recall: 0.9295 F1: 0.9268 Train AUC: 0.9856 Val AUC: 0.9694 Val PRC: 0.9628 Time: 0.23\n",
      "Epoch: 286 Train Loss: 0.1314 Acc: 0.9276 Pre: 0.9194 Recall: 0.9374 F1: 0.9283 Train AUC: 0.9865 Val AUC: 0.9743 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 287 Train Loss: 0.1252 Acc: 0.9286 Pre: 0.9179 Recall: 0.9413 F1: 0.9295 Train AUC: 0.9887 Val AUC: 0.9767 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 288 Train Loss: 0.1523 Acc: 0.9344 Pre: 0.9302 Recall: 0.9393 F1: 0.9348 Train AUC: 0.9875 Val AUC: 0.9779 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 289 Train Loss: 0.1317 Acc: 0.9315 Pre: 0.9216 Recall: 0.9432 F1: 0.9323 Train AUC: 0.9869 Val AUC: 0.9743 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 290 Train Loss: 0.1307 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9874 Val AUC: 0.9737 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 291 Train Loss: 0.1300 Acc: 0.9286 Pre: 0.9179 Recall: 0.9413 F1: 0.9295 Train AUC: 0.9864 Val AUC: 0.9771 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 292 Train Loss: 0.1099 Acc: 0.9276 Pre: 0.9115 Recall: 0.9472 F1: 0.9290 Train AUC: 0.9912 Val AUC: 0.9762 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 293 Train Loss: 0.1163 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9895 Val AUC: 0.9733 Val PRC: 0.9726 Time: 0.24\n",
      "Epoch: 294 Train Loss: 0.1165 Acc: 0.9305 Pre: 0.9183 Recall: 0.9452 F1: 0.9315 Train AUC: 0.9899 Val AUC: 0.9749 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 295 Train Loss: 0.1163 Acc: 0.9286 Pre: 0.9132 Recall: 0.9472 F1: 0.9299 Train AUC: 0.9903 Val AUC: 0.9770 Val PRC: 0.9713 Time: 0.24\n",
      "Epoch: 296 Train Loss: 0.1385 Acc: 0.9286 Pre: 0.9086 Recall: 0.9530 F1: 0.9303 Train AUC: 0.9861 Val AUC: 0.9773 Val PRC: 0.9779 Time: 0.25\n",
      "Epoch: 297 Train Loss: 0.1241 Acc: 0.9325 Pre: 0.9202 Recall: 0.9472 F1: 0.9335 Train AUC: 0.9882 Val AUC: 0.9771 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 298 Train Loss: 0.1251 Acc: 0.9286 Pre: 0.9328 Recall: 0.9237 F1: 0.9282 Train AUC: 0.9885 Val AUC: 0.9760 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 299 Train Loss: 0.1181 Acc: 0.9335 Pre: 0.9156 Recall: 0.9550 F1: 0.9349 Train AUC: 0.9895 Val AUC: 0.9761 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 300 Train Loss: 0.1168 Acc: 0.9295 Pre: 0.9279 Recall: 0.9315 F1: 0.9297 Train AUC: 0.9905 Val AUC: 0.9775 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 301 Train Loss: 0.1348 Acc: 0.9305 Pre: 0.9198 Recall: 0.9432 F1: 0.9314 Train AUC: 0.9872 Val AUC: 0.9759 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 302 Train Loss: 0.1183 Acc: 0.9276 Pre: 0.9130 Recall: 0.9452 F1: 0.9288 Train AUC: 0.9895 Val AUC: 0.9758 Val PRC: 0.9776 Time: 0.24\n",
      "Epoch: 303 Train Loss: 0.1308 Acc: 0.9286 Pre: 0.9086 Recall: 0.9530 F1: 0.9303 Train AUC: 0.9867 Val AUC: 0.9762 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 304 Train Loss: 0.1231 Acc: 0.9286 Pre: 0.9163 Recall: 0.9432 F1: 0.9296 Train AUC: 0.9883 Val AUC: 0.9774 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 305 Train Loss: 0.1239 Acc: 0.9247 Pre: 0.9064 Recall: 0.9472 F1: 0.9263 Train AUC: 0.9869 Val AUC: 0.9715 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 306 Train Loss: 0.1392 Acc: 0.9217 Pre: 0.8940 Recall: 0.9569 F1: 0.9244 Train AUC: 0.9847 Val AUC: 0.9726 Val PRC: 0.9728 Time: 0.24\n",
      "Epoch: 307 Train Loss: 0.1184 Acc: 0.9335 Pre: 0.9318 Recall: 0.9354 F1: 0.9336 Train AUC: 0.9901 Val AUC: 0.9769 Val PRC: 0.9795 Time: 0.24\n",
      "Epoch: 308 Train Loss: 0.1204 Acc: 0.9286 Pre: 0.9086 Recall: 0.9530 F1: 0.9303 Train AUC: 0.9892 Val AUC: 0.9749 Val PRC: 0.9775 Time: 0.24\n",
      "Epoch: 309 Train Loss: 0.1335 Acc: 0.9315 Pre: 0.9437 Recall: 0.9178 F1: 0.9306 Train AUC: 0.9876 Val AUC: 0.9765 Val PRC: 0.9784 Time: 0.24\n",
      "Epoch: 310 Train Loss: 0.1288 Acc: 0.9237 Pre: 0.9093 Recall: 0.9413 F1: 0.9250 Train AUC: 0.9876 Val AUC: 0.9734 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 311 Train Loss: 0.1313 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9877 Val AUC: 0.9728 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 312 Train Loss: 0.1137 Acc: 0.9256 Pre: 0.9273 Recall: 0.9237 F1: 0.9255 Train AUC: 0.9905 Val AUC: 0.9726 Val PRC: 0.9734 Time: 0.24\n",
      "Epoch: 313 Train Loss: 0.1158 Acc: 0.9344 Pre: 0.9205 Recall: 0.9511 F1: 0.9355 Train AUC: 0.9898 Val AUC: 0.9762 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 314 Train Loss: 0.1316 Acc: 0.9266 Pre: 0.9176 Recall: 0.9374 F1: 0.9274 Train AUC: 0.9873 Val AUC: 0.9757 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 315 Train Loss: 0.1193 Acc: 0.9335 Pre: 0.9171 Recall: 0.9530 F1: 0.9347 Train AUC: 0.9890 Val AUC: 0.9771 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 316 Train Loss: 0.1234 Acc: 0.9256 Pre: 0.9143 Recall: 0.9393 F1: 0.9266 Train AUC: 0.9880 Val AUC: 0.9756 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 317 Train Loss: 0.1368 Acc: 0.9247 Pre: 0.9110 Recall: 0.9413 F1: 0.9259 Train AUC: 0.9857 Val AUC: 0.9720 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 318 Train Loss: 0.1412 Acc: 0.9295 Pre: 0.9364 Recall: 0.9217 F1: 0.9290 Train AUC: 0.9845 Val AUC: 0.9740 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 319 Train Loss: 0.1071 Acc: 0.9266 Pre: 0.9241 Recall: 0.9295 F1: 0.9268 Train AUC: 0.9901 Val AUC: 0.9742 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 320 Train Loss: 0.1265 Acc: 0.9295 Pre: 0.9347 Recall: 0.9237 F1: 0.9291 Train AUC: 0.9882 Val AUC: 0.9735 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 321 Train Loss: 0.1189 Acc: 0.9247 Pre: 0.9375 Recall: 0.9100 F1: 0.9235 Train AUC: 0.9876 Val AUC: 0.9715 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 322 Train Loss: 0.1199 Acc: 0.9168 Pre: 0.9080 Recall: 0.9276 F1: 0.9177 Train AUC: 0.9891 Val AUC: 0.9718 Val PRC: 0.9738 Time: 0.24\n",
      "Epoch: 323 Train Loss: 0.1109 Acc: 0.9247 Pre: 0.9004 Recall: 0.9550 F1: 0.9269 Train AUC: 0.9904 Val AUC: 0.9737 Val PRC: 0.9754 Time: 0.23\n",
      "Epoch: 324 Train Loss: 0.1258 Acc: 0.9295 Pre: 0.9103 Recall: 0.9530 F1: 0.9312 Train AUC: 0.9879 Val AUC: 0.9766 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 325 Train Loss: 0.1206 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9889 Val AUC: 0.9727 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 326 Train Loss: 0.1019 Acc: 0.9295 Pre: 0.9165 Recall: 0.9452 F1: 0.9306 Train AUC: 0.9922 Val AUC: 0.9761 Val PRC: 0.9778 Time: 0.24\n",
      "Epoch: 327 Train Loss: 0.1108 Acc: 0.9305 Pre: 0.9231 Recall: 0.9393 F1: 0.9311 Train AUC: 0.9905 Val AUC: 0.9747 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 328 Train Loss: 0.1120 Acc: 0.9374 Pre: 0.9426 Recall: 0.9315 F1: 0.9370 Train AUC: 0.9907 Val AUC: 0.9766 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 329 Train Loss: 0.1122 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9898 Val AUC: 0.9754 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 330 Train Loss: 0.1140 Acc: 0.9384 Pre: 0.9392 Recall: 0.9374 F1: 0.9383 Train AUC: 0.9893 Val AUC: 0.9774 Val PRC: 0.9785 Time: 0.25\n",
      "Epoch: 331 Train Loss: 0.1058 Acc: 0.9335 Pre: 0.9335 Recall: 0.9335 F1: 0.9335 Train AUC: 0.9916 Val AUC: 0.9778 Val PRC: 0.9786 Time: 0.24\n",
      "Epoch: 332 Train Loss: 0.1289 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9865 Val AUC: 0.9746 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 333 Train Loss: 0.1180 Acc: 0.9305 Pre: 0.9365 Recall: 0.9237 F1: 0.9300 Train AUC: 0.9895 Val AUC: 0.9788 Val PRC: 0.9796 Time: 0.24\n",
      "Epoch: 334 Train Loss: 0.1035 Acc: 0.9276 Pre: 0.9361 Recall: 0.9178 F1: 0.9269 Train AUC: 0.9920 Val AUC: 0.9759 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 335 Train Loss: 0.1094 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9902 Val AUC: 0.9730 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 336 Train Loss: 0.1112 Acc: 0.9256 Pre: 0.9081 Recall: 0.9472 F1: 0.9272 Train AUC: 0.9903 Val AUC: 0.9727 Val PRC: 0.9734 Time: 0.24\n",
      "Epoch: 337 Train Loss: 0.1217 Acc: 0.9276 Pre: 0.9344 Recall: 0.9198 F1: 0.9270 Train AUC: 0.9882 Val AUC: 0.9786 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 338 Train Loss: 0.1131 Acc: 0.9256 Pre: 0.9240 Recall: 0.9276 F1: 0.9258 Train AUC: 0.9907 Val AUC: 0.9749 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 339 Train Loss: 0.1155 Acc: 0.9335 Pre: 0.9404 Recall: 0.9256 F1: 0.9329 Train AUC: 0.9892 Val AUC: 0.9769 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 340 Train Loss: 0.1131 Acc: 0.9266 Pre: 0.9082 Recall: 0.9491 F1: 0.9282 Train AUC: 0.9898 Val AUC: 0.9778 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 341 Train Loss: 0.1101 Acc: 0.9354 Pre: 0.9271 Recall: 0.9452 F1: 0.9360 Train AUC: 0.9905 Val AUC: 0.9754 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 342 Train Loss: 0.1132 Acc: 0.9295 Pre: 0.9381 Recall: 0.9198 F1: 0.9289 Train AUC: 0.9900 Val AUC: 0.9784 Val PRC: 0.9795 Time: 0.23\n",
      "Epoch: 343 Train Loss: 0.0986 Acc: 0.9335 Pre: 0.9439 Recall: 0.9217 F1: 0.9327 Train AUC: 0.9924 Val AUC: 0.9793 Val PRC: 0.9801 Time: 0.23\n",
      "Epoch: 344 Train Loss: 0.1128 Acc: 0.9237 Pre: 0.9124 Recall: 0.9374 F1: 0.9247 Train AUC: 0.9894 Val AUC: 0.9752 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 345 Train Loss: 0.1021 Acc: 0.9305 Pre: 0.9074 Recall: 0.9589 F1: 0.9324 Train AUC: 0.9919 Val AUC: 0.9764 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 346 Train Loss: 0.1196 Acc: 0.9256 Pre: 0.9065 Recall: 0.9491 F1: 0.9273 Train AUC: 0.9878 Val AUC: 0.9769 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 347 Train Loss: 0.1107 Acc: 0.9315 Pre: 0.9106 Recall: 0.9569 F1: 0.9332 Train AUC: 0.9902 Val AUC: 0.9784 Val PRC: 0.9804 Time: 0.23\n",
      "Epoch: 348 Train Loss: 0.1225 Acc: 0.9315 Pre: 0.9282 Recall: 0.9354 F1: 0.9318 Train AUC: 0.9878 Val AUC: 0.9734 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 349 Train Loss: 0.0993 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9921 Val AUC: 0.9750 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 350 Train Loss: 0.1003 Acc: 0.9315 Pre: 0.9298 Recall: 0.9335 F1: 0.9316 Train AUC: 0.9926 Val AUC: 0.9781 Val PRC: 0.9798 Time: 0.23\n",
      "Epoch: 351 Train Loss: 0.1058 Acc: 0.9344 Pre: 0.9302 Recall: 0.9393 F1: 0.9348 Train AUC: 0.9909 Val AUC: 0.9767 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 352 Train Loss: 0.1166 Acc: 0.9354 Pre: 0.9271 Recall: 0.9452 F1: 0.9360 Train AUC: 0.9893 Val AUC: 0.9757 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 353 Train Loss: 0.1076 Acc: 0.9325 Pre: 0.9154 Recall: 0.9530 F1: 0.9338 Train AUC: 0.9901 Val AUC: 0.9770 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 354 Train Loss: 0.1160 Acc: 0.9354 Pre: 0.9389 Recall: 0.9315 F1: 0.9352 Train AUC: 0.9892 Val AUC: 0.9755 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 355 Train Loss: 0.1096 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9909 Val AUC: 0.9786 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 356 Train Loss: 0.1232 Acc: 0.9354 Pre: 0.9190 Recall: 0.9550 F1: 0.9367 Train AUC: 0.9877 Val AUC: 0.9776 Val PRC: 0.9784 Time: 0.23\n",
      "Epoch: 357 Train Loss: 0.1159 Acc: 0.9325 Pre: 0.9350 Recall: 0.9295 F1: 0.9323 Train AUC: 0.9894 Val AUC: 0.9763 Val PRC: 0.9778 Time: 0.24\n",
      "Epoch: 358 Train Loss: 0.1012 Acc: 0.9325 Pre: 0.9266 Recall: 0.9393 F1: 0.9329 Train AUC: 0.9921 Val AUC: 0.9765 Val PRC: 0.9787 Time: 0.24\n",
      "Epoch: 359 Train Loss: 0.1131 Acc: 0.9305 Pre: 0.9183 Recall: 0.9452 F1: 0.9315 Train AUC: 0.9900 Val AUC: 0.9741 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 360 Train Loss: 0.1103 Acc: 0.9276 Pre: 0.9226 Recall: 0.9335 F1: 0.9280 Train AUC: 0.9909 Val AUC: 0.9751 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 361 Train Loss: 0.1085 Acc: 0.9286 Pre: 0.9041 Recall: 0.9589 F1: 0.9307 Train AUC: 0.9907 Val AUC: 0.9773 Val PRC: 0.9803 Time: 0.24\n",
      "Epoch: 362 Train Loss: 0.1185 Acc: 0.9305 Pre: 0.9167 Recall: 0.9472 F1: 0.9317 Train AUC: 0.9892 Val AUC: 0.9766 Val PRC: 0.9782 Time: 0.24\n",
      "Epoch: 363 Train Loss: 0.1131 Acc: 0.9354 Pre: 0.9337 Recall: 0.9374 F1: 0.9355 Train AUC: 0.9893 Val AUC: 0.9760 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 364 Train Loss: 0.0956 Acc: 0.9305 Pre: 0.9264 Recall: 0.9354 F1: 0.9309 Train AUC: 0.9922 Val AUC: 0.9751 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 365 Train Loss: 0.1100 Acc: 0.9325 Pre: 0.9234 Recall: 0.9432 F1: 0.9332 Train AUC: 0.9896 Val AUC: 0.9764 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 366 Train Loss: 0.1107 Acc: 0.9335 Pre: 0.9475 Recall: 0.9178 F1: 0.9324 Train AUC: 0.9902 Val AUC: 0.9767 Val PRC: 0.9791 Time: 0.23\n",
      "Epoch: 367 Train Loss: 0.1103 Acc: 0.9354 Pre: 0.9389 Recall: 0.9315 F1: 0.9352 Train AUC: 0.9886 Val AUC: 0.9777 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 368 Train Loss: 0.1100 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9912 Val AUC: 0.9724 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 369 Train Loss: 0.1057 Acc: 0.9286 Pre: 0.9311 Recall: 0.9256 F1: 0.9284 Train AUC: 0.9904 Val AUC: 0.9739 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 370 Train Loss: 0.1093 Acc: 0.9276 Pre: 0.9194 Recall: 0.9374 F1: 0.9283 Train AUC: 0.9914 Val AUC: 0.9722 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 371 Train Loss: 0.1154 Acc: 0.9305 Pre: 0.9215 Recall: 0.9413 F1: 0.9313 Train AUC: 0.9879 Val AUC: 0.9736 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 372 Train Loss: 0.1073 Acc: 0.9354 Pre: 0.9190 Recall: 0.9550 F1: 0.9367 Train AUC: 0.9907 Val AUC: 0.9799 Val PRC: 0.9813 Time: 0.24\n",
      "Epoch: 373 Train Loss: 0.0999 Acc: 0.9384 Pre: 0.9341 Recall: 0.9432 F1: 0.9387 Train AUC: 0.9922 Val AUC: 0.9756 Val PRC: 0.9773 Time: 0.23\n",
      "Epoch: 374 Train Loss: 0.1210 Acc: 0.9344 Pre: 0.9422 Recall: 0.9256 F1: 0.9339 Train AUC: 0.9873 Val AUC: 0.9754 Val PRC: 0.9775 Time: 0.25\n",
      "Epoch: 375 Train Loss: 0.1008 Acc: 0.9325 Pre: 0.9350 Recall: 0.9295 F1: 0.9323 Train AUC: 0.9920 Val AUC: 0.9758 Val PRC: 0.9781 Time: 0.24\n",
      "Epoch: 376 Train Loss: 0.1015 Acc: 0.9276 Pre: 0.9226 Recall: 0.9335 F1: 0.9280 Train AUC: 0.9903 Val AUC: 0.9730 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 377 Train Loss: 0.1170 Acc: 0.9344 Pre: 0.9422 Recall: 0.9256 F1: 0.9339 Train AUC: 0.9863 Val AUC: 0.9706 Val PRC: 0.9615 Time: 0.24\n",
      "Epoch: 378 Train Loss: 0.1008 Acc: 0.9315 Pre: 0.9076 Recall: 0.9609 F1: 0.9335 Train AUC: 0.9914 Val AUC: 0.9773 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 379 Train Loss: 0.1199 Acc: 0.9354 Pre: 0.9423 Recall: 0.9276 F1: 0.9349 Train AUC: 0.9899 Val AUC: 0.9766 Val PRC: 0.9776 Time: 0.24\n",
      "Epoch: 380 Train Loss: 0.1201 Acc: 0.9256 Pre: 0.9290 Recall: 0.9217 F1: 0.9253 Train AUC: 0.9887 Val AUC: 0.9762 Val PRC: 0.9775 Time: 0.24\n",
      "Epoch: 381 Train Loss: 0.0978 Acc: 0.9335 Pre: 0.9439 Recall: 0.9217 F1: 0.9327 Train AUC: 0.9934 Val AUC: 0.9757 Val PRC: 0.9783 Time: 0.24\n",
      "Epoch: 382 Train Loss: 0.1191 Acc: 0.9325 Pre: 0.9333 Recall: 0.9315 F1: 0.9324 Train AUC: 0.9894 Val AUC: 0.9744 Val PRC: 0.9760 Time: 0.24\n",
      "Epoch: 383 Train Loss: 0.0947 Acc: 0.9384 Pre: 0.9444 Recall: 0.9315 F1: 0.9379 Train AUC: 0.9927 Val AUC: 0.9771 Val PRC: 0.9792 Time: 0.24\n",
      "Epoch: 384 Train Loss: 0.1081 Acc: 0.9374 Pre: 0.9374 Recall: 0.9374 F1: 0.9374 Train AUC: 0.9917 Val AUC: 0.9765 Val PRC: 0.9783 Time: 0.24\n",
      "Epoch: 385 Train Loss: 0.0996 Acc: 0.9325 Pre: 0.9350 Recall: 0.9295 F1: 0.9323 Train AUC: 0.9909 Val AUC: 0.9748 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 386 Train Loss: 0.1035 Acc: 0.9374 Pre: 0.9323 Recall: 0.9432 F1: 0.9377 Train AUC: 0.9924 Val AUC: 0.9751 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 387 Train Loss: 0.1001 Acc: 0.9315 Pre: 0.9332 Recall: 0.9295 F1: 0.9314 Train AUC: 0.9919 Val AUC: 0.9758 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 388 Train Loss: 0.1122 Acc: 0.9364 Pre: 0.9460 Recall: 0.9256 F1: 0.9357 Train AUC: 0.9895 Val AUC: 0.9770 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 389 Train Loss: 0.1022 Acc: 0.9384 Pre: 0.9553 Recall: 0.9198 F1: 0.9372 Train AUC: 0.9909 Val AUC: 0.9771 Val PRC: 0.9795 Time: 0.24\n",
      "Epoch: 390 Train Loss: 0.0938 Acc: 0.9295 Pre: 0.9213 Recall: 0.9393 F1: 0.9302 Train AUC: 0.9933 Val AUC: 0.9751 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 391 Train Loss: 0.1242 Acc: 0.9335 Pre: 0.9421 Recall: 0.9237 F1: 0.9328 Train AUC: 0.9870 Val AUC: 0.9766 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 392 Train Loss: 0.1115 Acc: 0.9305 Pre: 0.9365 Recall: 0.9237 F1: 0.9300 Train AUC: 0.9883 Val AUC: 0.9775 Val PRC: 0.9790 Time: 0.24\n",
      "Epoch: 393 Train Loss: 0.1166 Acc: 0.9354 Pre: 0.9389 Recall: 0.9315 F1: 0.9352 Train AUC: 0.9889 Val AUC: 0.9783 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 394 Train Loss: 0.1135 Acc: 0.9335 Pre: 0.9268 Recall: 0.9413 F1: 0.9340 Train AUC: 0.9877 Val AUC: 0.9715 Val PRC: 0.9627 Time: 0.24\n",
      "Epoch: 395 Train Loss: 0.0947 Acc: 0.9315 Pre: 0.9315 Recall: 0.9315 F1: 0.9315 Train AUC: 0.9931 Val AUC: 0.9755 Val PRC: 0.9754 Time: 0.25\n",
      "Epoch: 396 Train Loss: 0.0942 Acc: 0.9354 Pre: 0.9406 Recall: 0.9295 F1: 0.9350 Train AUC: 0.9922 Val AUC: 0.9780 Val PRC: 0.9804 Time: 0.24\n",
      "Epoch: 397 Train Loss: 0.1206 Acc: 0.9335 Pre: 0.9475 Recall: 0.9178 F1: 0.9324 Train AUC: 0.9886 Val AUC: 0.9728 Val PRC: 0.9763 Time: 0.42\n",
      "Epoch: 398 Train Loss: 0.1101 Acc: 0.9374 Pre: 0.9479 Recall: 0.9256 F1: 0.9366 Train AUC: 0.9906 Val AUC: 0.9750 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 399 Train Loss: 0.1083 Acc: 0.9315 Pre: 0.9419 Recall: 0.9198 F1: 0.9307 Train AUC: 0.9907 Val AUC: 0.9743 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 400 Train Loss: 0.1124 Acc: 0.9295 Pre: 0.9149 Recall: 0.9472 F1: 0.9308 Train AUC: 0.9905 Val AUC: 0.9761 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 401 Train Loss: 0.1113 Acc: 0.9247 Pre: 0.9064 Recall: 0.9472 F1: 0.9263 Train AUC: 0.9897 Val AUC: 0.9715 Val PRC: 0.9725 Time: 0.31\n",
      "Epoch: 402 Train Loss: 0.1104 Acc: 0.9266 Pre: 0.9113 Recall: 0.9452 F1: 0.9280 Train AUC: 0.9894 Val AUC: 0.9736 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 403 Train Loss: 0.1081 Acc: 0.9305 Pre: 0.9453 Recall: 0.9139 F1: 0.9294 Train AUC: 0.9914 Val AUC: 0.9759 Val PRC: 0.9792 Time: 0.24\n",
      "Epoch: 404 Train Loss: 0.1031 Acc: 0.9295 Pre: 0.9416 Recall: 0.9159 F1: 0.9286 Train AUC: 0.9905 Val AUC: 0.9762 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 405 Train Loss: 0.1106 Acc: 0.9315 Pre: 0.9282 Recall: 0.9354 F1: 0.9318 Train AUC: 0.9898 Val AUC: 0.9782 Val PRC: 0.9797 Time: 0.24\n",
      "Epoch: 406 Train Loss: 0.0945 Acc: 0.9335 Pre: 0.9235 Recall: 0.9452 F1: 0.9342 Train AUC: 0.9914 Val AUC: 0.9770 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 407 Train Loss: 0.1206 Acc: 0.9325 Pre: 0.9402 Recall: 0.9237 F1: 0.9319 Train AUC: 0.9873 Val AUC: 0.9761 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 408 Train Loss: 0.1042 Acc: 0.9344 Pre: 0.9458 Recall: 0.9217 F1: 0.9336 Train AUC: 0.9899 Val AUC: 0.9769 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 409 Train Loss: 0.1247 Acc: 0.9315 Pre: 0.9349 Recall: 0.9276 F1: 0.9312 Train AUC: 0.9851 Val AUC: 0.9720 Val PRC: 0.9697 Time: 0.23\n",
      "Epoch: 410 Train Loss: 0.0907 Acc: 0.9335 Pre: 0.9439 Recall: 0.9217 F1: 0.9327 Train AUC: 0.9935 Val AUC: 0.9773 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 411 Train Loss: 0.1218 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9884 Val AUC: 0.9739 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 412 Train Loss: 0.1172 Acc: 0.9325 Pre: 0.9474 Recall: 0.9159 F1: 0.9313 Train AUC: 0.9901 Val AUC: 0.9700 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 413 Train Loss: 0.1086 Acc: 0.9305 Pre: 0.9231 Recall: 0.9393 F1: 0.9311 Train AUC: 0.9906 Val AUC: 0.9697 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 414 Train Loss: 0.1143 Acc: 0.9325 Pre: 0.9350 Recall: 0.9295 F1: 0.9323 Train AUC: 0.9900 Val AUC: 0.9724 Val PRC: 0.9719 Time: 0.23\n",
      "Epoch: 415 Train Loss: 0.1157 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9900 Val AUC: 0.9731 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 416 Train Loss: 0.1371 Acc: 0.9295 Pre: 0.9181 Recall: 0.9432 F1: 0.9305 Train AUC: 0.9864 Val AUC: 0.9743 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 417 Train Loss: 0.1024 Acc: 0.9354 Pre: 0.9271 Recall: 0.9452 F1: 0.9360 Train AUC: 0.9923 Val AUC: 0.9768 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 418 Train Loss: 0.1108 Acc: 0.9315 Pre: 0.9249 Recall: 0.9393 F1: 0.9320 Train AUC: 0.9902 Val AUC: 0.9771 Val PRC: 0.9782 Time: 0.23\n",
      "Epoch: 419 Train Loss: 0.1098 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9901 Val AUC: 0.9762 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 420 Train Loss: 0.1008 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9917 Val AUC: 0.9768 Val PRC: 0.9783 Time: 0.24\n",
      "Epoch: 421 Train Loss: 0.0919 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9934 Val AUC: 0.9744 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 422 Train Loss: 0.1062 Acc: 0.9305 Pre: 0.9231 Recall: 0.9393 F1: 0.9311 Train AUC: 0.9898 Val AUC: 0.9732 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 423 Train Loss: 0.0820 Acc: 0.9354 Pre: 0.9389 Recall: 0.9315 F1: 0.9352 Train AUC: 0.9945 Val AUC: 0.9783 Val PRC: 0.9799 Time: 0.24\n",
      "Epoch: 424 Train Loss: 0.1062 Acc: 0.9315 Pre: 0.9265 Recall: 0.9374 F1: 0.9319 Train AUC: 0.9898 Val AUC: 0.9771 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 425 Train Loss: 0.1197 Acc: 0.9344 Pre: 0.9302 Recall: 0.9393 F1: 0.9348 Train AUC: 0.9887 Val AUC: 0.9791 Val PRC: 0.9807 Time: 0.23\n",
      "Epoch: 426 Train Loss: 0.0958 Acc: 0.9344 Pre: 0.9370 Recall: 0.9315 F1: 0.9342 Train AUC: 0.9923 Val AUC: 0.9788 Val PRC: 0.9803 Time: 0.23\n",
      "Epoch: 427 Train Loss: 0.1042 Acc: 0.9364 Pre: 0.9373 Recall: 0.9354 F1: 0.9363 Train AUC: 0.9909 Val AUC: 0.9790 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 428 Train Loss: 0.0951 Acc: 0.9276 Pre: 0.9259 Recall: 0.9295 F1: 0.9277 Train AUC: 0.9921 Val AUC: 0.9746 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 429 Train Loss: 0.1137 Acc: 0.9384 Pre: 0.9516 Recall: 0.9237 F1: 0.9374 Train AUC: 0.9893 Val AUC: 0.9783 Val PRC: 0.9804 Time: 0.23\n",
      "Epoch: 430 Train Loss: 0.1139 Acc: 0.9305 Pre: 0.9365 Recall: 0.9237 F1: 0.9300 Train AUC: 0.9895 Val AUC: 0.9775 Val PRC: 0.9799 Time: 0.23\n",
      "Epoch: 431 Train Loss: 0.1007 Acc: 0.9315 Pre: 0.9168 Recall: 0.9491 F1: 0.9327 Train AUC: 0.9917 Val AUC: 0.9773 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 432 Train Loss: 0.0945 Acc: 0.9325 Pre: 0.9368 Recall: 0.9276 F1: 0.9322 Train AUC: 0.9922 Val AUC: 0.9771 Val PRC: 0.9788 Time: 0.24\n",
      "Epoch: 433 Train Loss: 0.0926 Acc: 0.9325 Pre: 0.9385 Recall: 0.9256 F1: 0.9320 Train AUC: 0.9925 Val AUC: 0.9761 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 434 Train Loss: 0.0992 Acc: 0.9393 Pre: 0.9463 Recall: 0.9315 F1: 0.9389 Train AUC: 0.9917 Val AUC: 0.9787 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 435 Train Loss: 0.1016 Acc: 0.9335 Pre: 0.9187 Recall: 0.9511 F1: 0.9346 Train AUC: 0.9912 Val AUC: 0.9773 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 436 Train Loss: 0.0948 Acc: 0.9384 Pre: 0.9444 Recall: 0.9315 F1: 0.9379 Train AUC: 0.9921 Val AUC: 0.9775 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 437 Train Loss: 0.1013 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9900 Val AUC: 0.9742 Val PRC: 0.9726 Time: 0.24\n",
      "Epoch: 438 Train Loss: 0.1038 Acc: 0.9393 Pre: 0.9572 Recall: 0.9198 F1: 0.9381 Train AUC: 0.9902 Val AUC: 0.9804 Val PRC: 0.9809 Time: 0.23\n",
      "Epoch: 439 Train Loss: 0.1041 Acc: 0.9364 Pre: 0.9305 Recall: 0.9432 F1: 0.9368 Train AUC: 0.9900 Val AUC: 0.9764 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 440 Train Loss: 0.1006 Acc: 0.9276 Pre: 0.9084 Recall: 0.9511 F1: 0.9293 Train AUC: 0.9903 Val AUC: 0.9760 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 441 Train Loss: 0.1082 Acc: 0.9315 Pre: 0.9137 Recall: 0.9530 F1: 0.9330 Train AUC: 0.9895 Val AUC: 0.9790 Val PRC: 0.9800 Time: 0.23\n",
      "Epoch: 442 Train Loss: 0.0922 Acc: 0.9354 Pre: 0.9371 Recall: 0.9335 F1: 0.9353 Train AUC: 0.9923 Val AUC: 0.9778 Val PRC: 0.9806 Time: 0.23\n",
      "Epoch: 443 Train Loss: 0.1094 Acc: 0.9384 Pre: 0.9392 Recall: 0.9374 F1: 0.9383 Train AUC: 0.9897 Val AUC: 0.9780 Val PRC: 0.9800 Time: 0.23\n",
      "Epoch: 444 Train Loss: 0.1002 Acc: 0.9354 Pre: 0.9320 Recall: 0.9393 F1: 0.9357 Train AUC: 0.9916 Val AUC: 0.9793 Val PRC: 0.9818 Time: 0.23\n",
      "Epoch: 445 Train Loss: 0.1070 Acc: 0.9325 Pre: 0.9474 Recall: 0.9159 F1: 0.9313 Train AUC: 0.9886 Val AUC: 0.9786 Val PRC: 0.9802 Time: 0.23\n",
      "Epoch: 446 Train Loss: 0.1003 Acc: 0.9315 Pre: 0.9315 Recall: 0.9315 F1: 0.9315 Train AUC: 0.9905 Val AUC: 0.9766 Val PRC: 0.9769 Time: 0.25\n",
      "Epoch: 447 Train Loss: 0.0893 Acc: 0.9295 Pre: 0.9416 Recall: 0.9159 F1: 0.9286 Train AUC: 0.9936 Val AUC: 0.9796 Val PRC: 0.9815 Time: 0.24\n",
      "Epoch: 448 Train Loss: 0.0855 Acc: 0.9295 Pre: 0.9312 Recall: 0.9276 F1: 0.9294 Train AUC: 0.9932 Val AUC: 0.9771 Val PRC: 0.9772 Time: 0.24\n",
      "Epoch: 449 Train Loss: 0.1000 Acc: 0.9335 Pre: 0.9284 Recall: 0.9393 F1: 0.9339 Train AUC: 0.9921 Val AUC: 0.9781 Val PRC: 0.9807 Time: 0.23\n",
      "Epoch: 450 Train Loss: 0.1012 Acc: 0.9335 Pre: 0.9251 Recall: 0.9432 F1: 0.9341 Train AUC: 0.9908 Val AUC: 0.9777 Val PRC: 0.9785 Time: 0.24\n",
      "Epoch: 451 Train Loss: 0.0942 Acc: 0.9364 Pre: 0.9373 Recall: 0.9354 F1: 0.9363 Train AUC: 0.9926 Val AUC: 0.9778 Val PRC: 0.9797 Time: 0.24\n",
      "Epoch: 452 Train Loss: 0.0895 Acc: 0.9354 Pre: 0.9423 Recall: 0.9276 F1: 0.9349 Train AUC: 0.9936 Val AUC: 0.9767 Val PRC: 0.9790 Time: 0.23\n",
      "Epoch: 453 Train Loss: 0.1055 Acc: 0.9344 Pre: 0.9237 Recall: 0.9472 F1: 0.9353 Train AUC: 0.9901 Val AUC: 0.9766 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 454 Train Loss: 0.0850 Acc: 0.9384 Pre: 0.9275 Recall: 0.9511 F1: 0.9391 Train AUC: 0.9935 Val AUC: 0.9783 Val PRC: 0.9803 Time: 0.23\n",
      "Epoch: 455 Train Loss: 0.0989 Acc: 0.9452 Pre: 0.9417 Recall: 0.9491 F1: 0.9454 Train AUC: 0.9915 Val AUC: 0.9788 Val PRC: 0.9811 Time: 0.23\n",
      "Epoch: 456 Train Loss: 0.0885 Acc: 0.9344 Pre: 0.9405 Recall: 0.9276 F1: 0.9340 Train AUC: 0.9932 Val AUC: 0.9764 Val PRC: 0.9784 Time: 0.23\n",
      "Epoch: 457 Train Loss: 0.0924 Acc: 0.9364 Pre: 0.9373 Recall: 0.9354 F1: 0.9363 Train AUC: 0.9917 Val AUC: 0.9786 Val PRC: 0.9806 Time: 0.24\n",
      "Epoch: 458 Train Loss: 0.0913 Acc: 0.9354 Pre: 0.9287 Recall: 0.9432 F1: 0.9359 Train AUC: 0.9925 Val AUC: 0.9778 Val PRC: 0.9790 Time: 0.24\n",
      "Epoch: 459 Train Loss: 0.0889 Acc: 0.9364 Pre: 0.9355 Recall: 0.9374 F1: 0.9365 Train AUC: 0.9928 Val AUC: 0.9770 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 460 Train Loss: 0.0970 Acc: 0.9325 Pre: 0.9420 Recall: 0.9217 F1: 0.9318 Train AUC: 0.9921 Val AUC: 0.9750 Val PRC: 0.9773 Time: 0.24\n",
      "Epoch: 461 Train Loss: 0.0917 Acc: 0.9432 Pre: 0.9331 Recall: 0.9550 F1: 0.9439 Train AUC: 0.9923 Val AUC: 0.9775 Val PRC: 0.9805 Time: 0.23\n",
      "Epoch: 462 Train Loss: 0.0971 Acc: 0.9354 Pre: 0.9337 Recall: 0.9374 F1: 0.9355 Train AUC: 0.9911 Val AUC: 0.9752 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 463 Train Loss: 0.0982 Acc: 0.9315 Pre: 0.9332 Recall: 0.9295 F1: 0.9314 Train AUC: 0.9908 Val AUC: 0.9739 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 464 Train Loss: 0.0855 Acc: 0.9305 Pre: 0.9280 Recall: 0.9335 F1: 0.9307 Train AUC: 0.9933 Val AUC: 0.9749 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 465 Train Loss: 0.0895 Acc: 0.9423 Pre: 0.9449 Recall: 0.9393 F1: 0.9421 Train AUC: 0.9925 Val AUC: 0.9771 Val PRC: 0.9794 Time: 0.24\n",
      "Epoch: 466 Train Loss: 0.0857 Acc: 0.9256 Pre: 0.9207 Recall: 0.9315 F1: 0.9261 Train AUC: 0.9922 Val AUC: 0.9755 Val PRC: 0.9767 Time: 0.24\n",
      "Epoch: 467 Train Loss: 0.0869 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9927 Val AUC: 0.9784 Val PRC: 0.9808 Time: 0.23\n",
      "Epoch: 468 Train Loss: 0.1127 Acc: 0.9344 Pre: 0.9370 Recall: 0.9315 F1: 0.9342 Train AUC: 0.9887 Val AUC: 0.9793 Val PRC: 0.9820 Time: 0.23\n",
      "Epoch: 469 Train Loss: 0.0774 Acc: 0.9374 Pre: 0.9515 Recall: 0.9217 F1: 0.9364 Train AUC: 0.9946 Val AUC: 0.9786 Val PRC: 0.9806 Time: 0.24\n",
      "Epoch: 470 Train Loss: 0.1089 Acc: 0.9384 Pre: 0.9462 Recall: 0.9295 F1: 0.9378 Train AUC: 0.9928 Val AUC: 0.9761 Val PRC: 0.9791 Time: 0.24\n",
      "Epoch: 471 Train Loss: 0.0951 Acc: 0.9344 Pre: 0.9221 Recall: 0.9491 F1: 0.9354 Train AUC: 0.9914 Val AUC: 0.9757 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 472 Train Loss: 0.0819 Acc: 0.9364 Pre: 0.9425 Recall: 0.9295 F1: 0.9360 Train AUC: 0.9939 Val AUC: 0.9777 Val PRC: 0.9801 Time: 0.23\n",
      "Epoch: 473 Train Loss: 0.1057 Acc: 0.9295 Pre: 0.9399 Recall: 0.9178 F1: 0.9287 Train AUC: 0.9910 Val AUC: 0.9748 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 474 Train Loss: 0.0972 Acc: 0.9384 Pre: 0.9409 Recall: 0.9354 F1: 0.9382 Train AUC: 0.9910 Val AUC: 0.9765 Val PRC: 0.9798 Time: 0.23\n",
      "Epoch: 475 Train Loss: 0.0845 Acc: 0.9335 Pre: 0.9457 Recall: 0.9198 F1: 0.9325 Train AUC: 0.9932 Val AUC: 0.9769 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 476 Train Loss: 0.0866 Acc: 0.9315 Pre: 0.9349 Recall: 0.9276 F1: 0.9312 Train AUC: 0.9938 Val AUC: 0.9757 Val PRC: 0.9777 Time: 0.24\n",
      "Epoch: 477 Train Loss: 0.0809 Acc: 0.9335 Pre: 0.9335 Recall: 0.9335 F1: 0.9335 Train AUC: 0.9927 Val AUC: 0.9728 Val PRC: 0.9720 Time: 0.24\n",
      "Epoch: 478 Train Loss: 0.0873 Acc: 0.9335 Pre: 0.9511 Recall: 0.9139 F1: 0.9321 Train AUC: 0.9937 Val AUC: 0.9770 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 479 Train Loss: 0.0865 Acc: 0.9354 Pre: 0.9495 Recall: 0.9198 F1: 0.9344 Train AUC: 0.9931 Val AUC: 0.9752 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 480 Train Loss: 0.0775 Acc: 0.9305 Pre: 0.9331 Recall: 0.9276 F1: 0.9303 Train AUC: 0.9946 Val AUC: 0.9767 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 481 Train Loss: 0.0893 Acc: 0.9364 Pre: 0.9339 Recall: 0.9393 F1: 0.9366 Train AUC: 0.9922 Val AUC: 0.9773 Val PRC: 0.9791 Time: 0.23\n",
      "Epoch: 482 Train Loss: 0.0827 Acc: 0.9344 Pre: 0.9387 Recall: 0.9295 F1: 0.9341 Train AUC: 0.9929 Val AUC: 0.9749 Val PRC: 0.9773 Time: 0.23\n",
      "Epoch: 483 Train Loss: 0.0856 Acc: 0.9335 Pre: 0.9421 Recall: 0.9237 F1: 0.9328 Train AUC: 0.9941 Val AUC: 0.9770 Val PRC: 0.9799 Time: 0.23\n",
      "Epoch: 484 Train Loss: 0.0996 Acc: 0.9335 Pre: 0.9235 Recall: 0.9452 F1: 0.9342 Train AUC: 0.9910 Val AUC: 0.9764 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 485 Train Loss: 0.0764 Acc: 0.9364 Pre: 0.9478 Recall: 0.9237 F1: 0.9356 Train AUC: 0.9944 Val AUC: 0.9786 Val PRC: 0.9814 Time: 0.23\n",
      "Epoch: 486 Train Loss: 0.1045 Acc: 0.9325 Pre: 0.9316 Recall: 0.9335 F1: 0.9326 Train AUC: 0.9897 Val AUC: 0.9759 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 487 Train Loss: 0.0891 Acc: 0.9364 Pre: 0.9442 Recall: 0.9276 F1: 0.9358 Train AUC: 0.9926 Val AUC: 0.9783 Val PRC: 0.9816 Time: 0.23\n",
      "Epoch: 488 Train Loss: 0.0824 Acc: 0.9325 Pre: 0.9300 Recall: 0.9354 F1: 0.9327 Train AUC: 0.9944 Val AUC: 0.9758 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 489 Train Loss: 0.0887 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9929 Val AUC: 0.9760 Val PRC: 0.9792 Time: 0.24\n",
      "Epoch: 490 Train Loss: 0.0817 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9939 Val AUC: 0.9737 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 491 Train Loss: 0.0895 Acc: 0.9325 Pre: 0.9566 Recall: 0.9061 F1: 0.9307 Train AUC: 0.9926 Val AUC: 0.9740 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 492 Train Loss: 0.0771 Acc: 0.9315 Pre: 0.9298 Recall: 0.9335 F1: 0.9316 Train AUC: 0.9946 Val AUC: 0.9731 Val PRC: 0.9709 Time: 0.23\n",
      "Epoch: 493 Train Loss: 0.0839 Acc: 0.9286 Pre: 0.9179 Recall: 0.9413 F1: 0.9295 Train AUC: 0.9935 Val AUC: 0.9724 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 494 Train Loss: 0.0852 Acc: 0.9315 Pre: 0.9401 Recall: 0.9217 F1: 0.9308 Train AUC: 0.9933 Val AUC: 0.9735 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 495 Train Loss: 0.0796 Acc: 0.9256 Pre: 0.9207 Recall: 0.9315 F1: 0.9261 Train AUC: 0.9944 Val AUC: 0.9736 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 496 Train Loss: 0.0775 Acc: 0.9335 Pre: 0.9511 Recall: 0.9139 F1: 0.9321 Train AUC: 0.9945 Val AUC: 0.9746 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 497 Train Loss: 0.0806 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9935 Val AUC: 0.9726 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 498 Train Loss: 0.0830 Acc: 0.9315 Pre: 0.9332 Recall: 0.9295 F1: 0.9314 Train AUC: 0.9922 Val AUC: 0.9719 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 499 Train Loss: 0.0801 Acc: 0.9305 Pre: 0.9400 Recall: 0.9198 F1: 0.9298 Train AUC: 0.9933 Val AUC: 0.9741 Val PRC: 0.9789 Time: 0.24\n",
      "Epoch: 500 Train Loss: 0.0755 Acc: 0.9325 Pre: 0.9420 Recall: 0.9217 F1: 0.9318 Train AUC: 0.9947 Val AUC: 0.9765 Val PRC: 0.9794 Time: 0.23\n",
      "Fold: 1 Best Epoch: 444 Val acc: 0.9354 Val Pre: 0.9320 Val Recall: 0.9393 Val F1: 0.9357 Val AUC: 0.9793 Val PRC: 0.9818\n",
      "------this is 2th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6955 Acc: 0.5010 Pre: 0.5005 Recall: 1.0000 F1: 0.6671 Train AUC: 0.5467 Val AUC: 0.5317 Val PRC: 0.5017 Time: 0.23\n",
      "Epoch: 2 Train Loss: 0.6838 Acc: 0.5245 Pre: 0.5132 Recall: 0.9530 F1: 0.6671 Train AUC: 0.6146 Val AUC: 0.6094 Val PRC: 0.5796 Time: 0.24\n",
      "Epoch: 3 Train Loss: 0.7081 Acc: 0.5020 Pre: 0.5010 Recall: 0.9961 F1: 0.6667 Train AUC: 0.4548 Val AUC: 0.4570 Val PRC: 0.4811 Time: 0.23\n",
      "Epoch: 4 Train Loss: 0.6733 Acc: 0.5176 Pre: 0.5092 Recall: 0.9765 F1: 0.6693 Train AUC: 0.6574 Val AUC: 0.6357 Val PRC: 0.5996 Time: 0.24\n",
      "Epoch: 5 Train Loss: 0.6838 Acc: 0.5352 Pre: 0.5190 Recall: 0.9628 F1: 0.6744 Train AUC: 0.6079 Val AUC: 0.6223 Val PRC: 0.5812 Time: 0.23\n",
      "Epoch: 6 Train Loss: 0.6670 Acc: 0.6184 Pre: 0.5765 Recall: 0.8924 F1: 0.7005 Train AUC: 0.6833 Val AUC: 0.6927 Val PRC: 0.6401 Time: 0.23\n",
      "Epoch: 7 Train Loss: 0.6714 Acc: 0.5939 Pre: 0.5593 Recall: 0.8865 F1: 0.6858 Train AUC: 0.6680 Val AUC: 0.6476 Val PRC: 0.6020 Time: 0.23\n",
      "Epoch: 8 Train Loss: 0.6597 Acc: 0.6272 Pre: 0.5840 Recall: 0.8845 F1: 0.7035 Train AUC: 0.7065 Val AUC: 0.7023 Val PRC: 0.6682 Time: 0.23\n",
      "Epoch: 9 Train Loss: 0.6502 Acc: 0.6781 Pre: 0.6338 Recall: 0.8434 F1: 0.7238 Train AUC: 0.7372 Val AUC: 0.7320 Val PRC: 0.6893 Time: 0.23\n",
      "Epoch: 10 Train Loss: 0.6490 Acc: 0.7114 Pre: 0.6736 Recall: 0.8200 F1: 0.7396 Train AUC: 0.7384 Val AUC: 0.7409 Val PRC: 0.6728 Time: 0.24\n",
      "Epoch: 11 Train Loss: 0.6312 Acc: 0.7211 Pre: 0.6697 Recall: 0.8728 F1: 0.7579 Train AUC: 0.7918 Val AUC: 0.7862 Val PRC: 0.7320 Time: 0.24\n",
      "Epoch: 12 Train Loss: 0.6402 Acc: 0.7123 Pre: 0.6472 Recall: 0.9335 F1: 0.7644 Train AUC: 0.7501 Val AUC: 0.7618 Val PRC: 0.6794 Time: 0.24\n",
      "Epoch: 13 Train Loss: 0.6293 Acc: 0.7280 Pre: 0.6681 Recall: 0.9061 F1: 0.7691 Train AUC: 0.7749 Val AUC: 0.7658 Val PRC: 0.6896 Time: 0.23\n",
      "Epoch: 14 Train Loss: 0.6368 Acc: 0.6751 Pre: 0.6158 Recall: 0.9315 F1: 0.7414 Train AUC: 0.7209 Val AUC: 0.7032 Val PRC: 0.6432 Time: 0.24\n",
      "Epoch: 15 Train Loss: 0.6144 Acc: 0.7231 Pre: 0.6583 Recall: 0.9276 F1: 0.7701 Train AUC: 0.7780 Val AUC: 0.7600 Val PRC: 0.6996 Time: 0.24\n",
      "Epoch: 16 Train Loss: 0.6047 Acc: 0.7466 Pre: 0.6821 Recall: 0.9237 F1: 0.7847 Train AUC: 0.7857 Val AUC: 0.7789 Val PRC: 0.7190 Time: 0.23\n",
      "Epoch: 17 Train Loss: 0.6070 Acc: 0.7065 Pre: 0.6401 Recall: 0.9432 F1: 0.7627 Train AUC: 0.7631 Val AUC: 0.7434 Val PRC: 0.7143 Time: 0.24\n",
      "Epoch: 18 Train Loss: 0.5944 Acc: 0.7280 Pre: 0.6616 Recall: 0.9335 F1: 0.7744 Train AUC: 0.7802 Val AUC: 0.7655 Val PRC: 0.7430 Time: 0.23\n",
      "Epoch: 19 Train Loss: 0.5830 Acc: 0.7446 Pre: 0.6746 Recall: 0.9452 F1: 0.7873 Train AUC: 0.7983 Val AUC: 0.7927 Val PRC: 0.7533 Time: 0.23\n",
      "Epoch: 20 Train Loss: 0.5745 Acc: 0.7603 Pre: 0.6967 Recall: 0.9217 F1: 0.7936 Train AUC: 0.8095 Val AUC: 0.7932 Val PRC: 0.7467 Time: 0.23\n",
      "Epoch: 21 Train Loss: 0.5666 Acc: 0.7466 Pre: 0.6805 Recall: 0.9295 F1: 0.7858 Train AUC: 0.8167 Val AUC: 0.8244 Val PRC: 0.7978 Time: 0.24\n",
      "Epoch: 22 Train Loss: 0.5634 Acc: 0.7554 Pre: 0.6905 Recall: 0.9256 F1: 0.7910 Train AUC: 0.8204 Val AUC: 0.8193 Val PRC: 0.7741 Time: 0.23\n",
      "Epoch: 23 Train Loss: 0.5591 Acc: 0.7505 Pre: 0.6922 Recall: 0.9022 F1: 0.7833 Train AUC: 0.8068 Val AUC: 0.7947 Val PRC: 0.7471 Time: 0.23\n",
      "Epoch: 24 Train Loss: 0.5601 Acc: 0.7436 Pre: 0.6791 Recall: 0.9237 F1: 0.7828 Train AUC: 0.7944 Val AUC: 0.7844 Val PRC: 0.7507 Time: 0.23\n",
      "Epoch: 25 Train Loss: 0.5272 Acc: 0.7847 Pre: 0.7299 Recall: 0.9041 F1: 0.8077 Train AUC: 0.8671 Val AUC: 0.8599 Val PRC: 0.8448 Time: 0.23\n",
      "Epoch: 26 Train Loss: 0.4995 Acc: 0.7926 Pre: 0.7384 Recall: 0.9061 F1: 0.8137 Train AUC: 0.8828 Val AUC: 0.8793 Val PRC: 0.8861 Time: 0.24\n",
      "Epoch: 27 Train Loss: 0.5298 Acc: 0.8004 Pre: 0.7776 Recall: 0.8415 F1: 0.8083 Train AUC: 0.8538 Val AUC: 0.8595 Val PRC: 0.8281 Time: 0.40\n",
      "Epoch: 28 Train Loss: 0.4816 Acc: 0.8063 Pre: 0.7893 Recall: 0.8356 F1: 0.8118 Train AUC: 0.8912 Val AUC: 0.8888 Val PRC: 0.8920 Time: 0.24\n",
      "Epoch: 29 Train Loss: 0.4896 Acc: 0.8072 Pre: 0.7688 Recall: 0.8787 F1: 0.8201 Train AUC: 0.8812 Val AUC: 0.8953 Val PRC: 0.8926 Time: 0.24\n",
      "Epoch: 30 Train Loss: 0.4699 Acc: 0.8151 Pre: 0.7917 Recall: 0.8552 F1: 0.8222 Train AUC: 0.8945 Val AUC: 0.9003 Val PRC: 0.9033 Time: 0.23\n",
      "Epoch: 31 Train Loss: 0.4560 Acc: 0.8023 Pre: 0.8096 Recall: 0.7906 F1: 0.8000 Train AUC: 0.9035 Val AUC: 0.8919 Val PRC: 0.8950 Time: 0.23\n",
      "Epoch: 32 Train Loss: 0.4439 Acc: 0.8102 Pre: 0.8241 Recall: 0.7886 F1: 0.8060 Train AUC: 0.8969 Val AUC: 0.8936 Val PRC: 0.9044 Time: 0.23\n",
      "Epoch: 33 Train Loss: 0.4347 Acc: 0.8053 Pre: 0.7776 Recall: 0.8552 F1: 0.8145 Train AUC: 0.9000 Val AUC: 0.8993 Val PRC: 0.9112 Time: 0.23\n",
      "Epoch: 34 Train Loss: 0.4262 Acc: 0.7975 Pre: 0.7603 Recall: 0.8689 F1: 0.8110 Train AUC: 0.8988 Val AUC: 0.9046 Val PRC: 0.9152 Time: 0.23\n",
      "Epoch: 35 Train Loss: 0.4115 Acc: 0.8200 Pre: 0.8330 Recall: 0.8004 F1: 0.8164 Train AUC: 0.9062 Val AUC: 0.9049 Val PRC: 0.9088 Time: 0.23\n",
      "Epoch: 36 Train Loss: 0.4053 Acc: 0.8337 Pre: 0.8884 Recall: 0.7632 F1: 0.8211 Train AUC: 0.9018 Val AUC: 0.9097 Val PRC: 0.9229 Time: 0.23\n",
      "Epoch: 37 Train Loss: 0.3983 Acc: 0.8033 Pre: 0.7892 Recall: 0.8278 F1: 0.8080 Train AUC: 0.9088 Val AUC: 0.9062 Val PRC: 0.9150 Time: 0.24\n",
      "Epoch: 38 Train Loss: 0.3919 Acc: 0.8239 Pre: 0.8637 Recall: 0.7691 F1: 0.8137 Train AUC: 0.9047 Val AUC: 0.9140 Val PRC: 0.9225 Time: 0.23\n",
      "Epoch: 39 Train Loss: 0.3720 Acc: 0.8209 Pre: 0.7918 Recall: 0.8708 F1: 0.8295 Train AUC: 0.9187 Val AUC: 0.9195 Val PRC: 0.9258 Time: 0.24\n",
      "Epoch: 40 Train Loss: 0.3694 Acc: 0.8190 Pre: 0.7870 Recall: 0.8748 F1: 0.8285 Train AUC: 0.9167 Val AUC: 0.9230 Val PRC: 0.9301 Time: 0.24\n",
      "Epoch: 41 Train Loss: 0.3799 Acc: 0.8268 Pre: 0.8744 Recall: 0.7632 F1: 0.8150 Train AUC: 0.9083 Val AUC: 0.9101 Val PRC: 0.9202 Time: 0.23\n",
      "Epoch: 42 Train Loss: 0.3698 Acc: 0.8014 Pre: 0.7484 Recall: 0.9080 F1: 0.8205 Train AUC: 0.9108 Val AUC: 0.9195 Val PRC: 0.9277 Time: 0.23\n",
      "Epoch: 43 Train Loss: 0.3524 Acc: 0.8405 Pre: 0.9009 Recall: 0.7652 F1: 0.8275 Train AUC: 0.9191 Val AUC: 0.9199 Val PRC: 0.9295 Time: 0.23\n",
      "Epoch: 44 Train Loss: 0.3546 Acc: 0.8121 Pre: 0.7663 Recall: 0.8982 F1: 0.8270 Train AUC: 0.9183 Val AUC: 0.9269 Val PRC: 0.9340 Time: 0.24\n",
      "Epoch: 45 Train Loss: 0.3348 Acc: 0.8601 Pre: 0.8932 Recall: 0.8180 F1: 0.8539 Train AUC: 0.9306 Val AUC: 0.9356 Val PRC: 0.9432 Time: 0.24\n",
      "Epoch: 46 Train Loss: 0.3273 Acc: 0.8611 Pre: 0.8901 Recall: 0.8239 F1: 0.8557 Train AUC: 0.9309 Val AUC: 0.9358 Val PRC: 0.9428 Time: 0.23\n",
      "Epoch: 47 Train Loss: 0.3237 Acc: 0.8493 Pre: 0.8182 Recall: 0.8982 F1: 0.8563 Train AUC: 0.9330 Val AUC: 0.9388 Val PRC: 0.9448 Time: 0.24\n",
      "Epoch: 48 Train Loss: 0.3312 Acc: 0.8601 Pre: 0.8866 Recall: 0.8258 F1: 0.8551 Train AUC: 0.9302 Val AUC: 0.9416 Val PRC: 0.9486 Time: 0.24\n",
      "Epoch: 49 Train Loss: 0.3306 Acc: 0.8679 Pre: 0.9034 Recall: 0.8239 F1: 0.8618 Train AUC: 0.9287 Val AUC: 0.9389 Val PRC: 0.9459 Time: 0.23\n",
      "Epoch: 50 Train Loss: 0.3171 Acc: 0.8611 Pre: 0.8742 Recall: 0.8434 F1: 0.8586 Train AUC: 0.9359 Val AUC: 0.9414 Val PRC: 0.9455 Time: 0.23\n",
      "Epoch: 51 Train Loss: 0.3179 Acc: 0.8630 Pre: 0.8825 Recall: 0.8376 F1: 0.8594 Train AUC: 0.9333 Val AUC: 0.9425 Val PRC: 0.9490 Time: 0.23\n",
      "Epoch: 52 Train Loss: 0.3149 Acc: 0.8669 Pre: 0.8627 Recall: 0.8728 F1: 0.8677 Train AUC: 0.9363 Val AUC: 0.9436 Val PRC: 0.9467 Time: 0.23\n",
      "Epoch: 53 Train Loss: 0.3284 Acc: 0.8659 Pre: 0.8929 Recall: 0.8317 F1: 0.8612 Train AUC: 0.9313 Val AUC: 0.9405 Val PRC: 0.9464 Time: 0.23\n",
      "Epoch: 54 Train Loss: 0.3229 Acc: 0.8738 Pre: 0.8820 Recall: 0.8630 F1: 0.8724 Train AUC: 0.9344 Val AUC: 0.9450 Val PRC: 0.9455 Time: 0.23\n",
      "Epoch: 55 Train Loss: 0.3306 Acc: 0.8640 Pre: 0.8605 Recall: 0.8689 F1: 0.8647 Train AUC: 0.9330 Val AUC: 0.9481 Val PRC: 0.9528 Time: 0.23\n",
      "Epoch: 56 Train Loss: 0.3123 Acc: 0.8679 Pre: 0.8806 Recall: 0.8513 F1: 0.8657 Train AUC: 0.9378 Val AUC: 0.9452 Val PRC: 0.9517 Time: 0.23\n",
      "Epoch: 57 Train Loss: 0.3076 Acc: 0.8728 Pre: 0.9224 Recall: 0.8141 F1: 0.8649 Train AUC: 0.9391 Val AUC: 0.9466 Val PRC: 0.9509 Time: 0.24\n",
      "Epoch: 58 Train Loss: 0.3069 Acc: 0.8659 Pre: 0.8710 Recall: 0.8591 F1: 0.8650 Train AUC: 0.9391 Val AUC: 0.9460 Val PRC: 0.9521 Time: 0.24\n",
      "Epoch: 59 Train Loss: 0.3060 Acc: 0.8738 Pre: 0.8702 Recall: 0.8787 F1: 0.8744 Train AUC: 0.9402 Val AUC: 0.9450 Val PRC: 0.9503 Time: 0.23\n",
      "Epoch: 60 Train Loss: 0.3070 Acc: 0.8689 Pre: 0.8839 Recall: 0.8493 F1: 0.8663 Train AUC: 0.9383 Val AUC: 0.9492 Val PRC: 0.9536 Time: 0.23\n",
      "Epoch: 61 Train Loss: 0.3025 Acc: 0.8748 Pre: 0.9118 Recall: 0.8297 F1: 0.8689 Train AUC: 0.9411 Val AUC: 0.9474 Val PRC: 0.9514 Time: 0.23\n",
      "Epoch: 62 Train Loss: 0.2999 Acc: 0.8767 Pre: 0.9212 Recall: 0.8239 F1: 0.8698 Train AUC: 0.9426 Val AUC: 0.9477 Val PRC: 0.9528 Time: 0.23\n",
      "Epoch: 63 Train Loss: 0.2930 Acc: 0.8796 Pre: 0.9025 Recall: 0.8513 F1: 0.8761 Train AUC: 0.9439 Val AUC: 0.9526 Val PRC: 0.9569 Time: 0.23\n",
      "Epoch: 64 Train Loss: 0.2936 Acc: 0.8787 Pre: 0.8728 Recall: 0.8865 F1: 0.8796 Train AUC: 0.9442 Val AUC: 0.9540 Val PRC: 0.9584 Time: 0.23\n",
      "Epoch: 65 Train Loss: 0.2836 Acc: 0.8748 Pre: 0.8884 Recall: 0.8571 F1: 0.8725 Train AUC: 0.9479 Val AUC: 0.9481 Val PRC: 0.9530 Time: 0.23\n",
      "Epoch: 66 Train Loss: 0.2842 Acc: 0.8757 Pre: 0.8871 Recall: 0.8611 F1: 0.8739 Train AUC: 0.9470 Val AUC: 0.9516 Val PRC: 0.9560 Time: 0.23\n",
      "Epoch: 67 Train Loss: 0.2835 Acc: 0.8757 Pre: 0.8855 Recall: 0.8630 F1: 0.8741 Train AUC: 0.9474 Val AUC: 0.9501 Val PRC: 0.9544 Time: 0.24\n",
      "Epoch: 68 Train Loss: 0.2825 Acc: 0.8689 Pre: 0.8935 Recall: 0.8376 F1: 0.8646 Train AUC: 0.9490 Val AUC: 0.9482 Val PRC: 0.9525 Time: 0.23\n",
      "Epoch: 69 Train Loss: 0.2743 Acc: 0.8767 Pre: 0.8937 Recall: 0.8552 F1: 0.8740 Train AUC: 0.9518 Val AUC: 0.9503 Val PRC: 0.9532 Time: 0.23\n",
      "Epoch: 70 Train Loss: 0.2711 Acc: 0.8679 Pre: 0.8806 Recall: 0.8513 F1: 0.8657 Train AUC: 0.9524 Val AUC: 0.9473 Val PRC: 0.9482 Time: 0.23\n",
      "Epoch: 71 Train Loss: 0.2755 Acc: 0.8757 Pre: 0.8765 Recall: 0.8748 F1: 0.8756 Train AUC: 0.9530 Val AUC: 0.9497 Val PRC: 0.9532 Time: 0.23\n",
      "Epoch: 72 Train Loss: 0.2785 Acc: 0.8718 Pre: 0.8585 Recall: 0.8904 F1: 0.8742 Train AUC: 0.9496 Val AUC: 0.9492 Val PRC: 0.9517 Time: 0.23\n",
      "Epoch: 73 Train Loss: 0.2794 Acc: 0.8689 Pre: 0.8563 Recall: 0.8865 F1: 0.8712 Train AUC: 0.9498 Val AUC: 0.9475 Val PRC: 0.9440 Time: 0.23\n",
      "Epoch: 74 Train Loss: 0.2669 Acc: 0.8855 Pre: 0.9121 Recall: 0.8532 F1: 0.8817 Train AUC: 0.9558 Val AUC: 0.9555 Val PRC: 0.9589 Time: 0.23\n",
      "Epoch: 75 Train Loss: 0.2625 Acc: 0.8816 Pre: 0.8963 Recall: 0.8630 F1: 0.8794 Train AUC: 0.9567 Val AUC: 0.9541 Val PRC: 0.9558 Time: 0.23\n",
      "Epoch: 76 Train Loss: 0.2793 Acc: 0.8718 Pre: 0.8612 Recall: 0.8865 F1: 0.8737 Train AUC: 0.9502 Val AUC: 0.9515 Val PRC: 0.9543 Time: 0.23\n",
      "Epoch: 77 Train Loss: 0.2588 Acc: 0.8885 Pre: 0.9197 Recall: 0.8513 F1: 0.8841 Train AUC: 0.9577 Val AUC: 0.9544 Val PRC: 0.9586 Time: 0.24\n",
      "Epoch: 78 Train Loss: 0.2659 Acc: 0.8581 Pre: 0.8123 Recall: 0.9315 F1: 0.8678 Train AUC: 0.9545 Val AUC: 0.9515 Val PRC: 0.9485 Time: 0.23\n",
      "Epoch: 79 Train Loss: 0.2646 Acc: 0.8845 Pre: 0.8816 Recall: 0.8885 F1: 0.8850 Train AUC: 0.9569 Val AUC: 0.9531 Val PRC: 0.9531 Time: 0.23\n",
      "Epoch: 80 Train Loss: 0.2561 Acc: 0.8865 Pre: 0.8990 Recall: 0.8708 F1: 0.8847 Train AUC: 0.9593 Val AUC: 0.9541 Val PRC: 0.9537 Time: 0.23\n",
      "Epoch: 81 Train Loss: 0.2619 Acc: 0.8777 Pre: 0.8601 Recall: 0.9022 F1: 0.8806 Train AUC: 0.9575 Val AUC: 0.9509 Val PRC: 0.9487 Time: 0.23\n",
      "Epoch: 82 Train Loss: 0.2600 Acc: 0.8787 Pre: 0.8893 Recall: 0.8650 F1: 0.8770 Train AUC: 0.9577 Val AUC: 0.9495 Val PRC: 0.9516 Time: 0.24\n",
      "Epoch: 83 Train Loss: 0.2526 Acc: 0.8806 Pre: 0.8867 Recall: 0.8728 F1: 0.8797 Train AUC: 0.9609 Val AUC: 0.9531 Val PRC: 0.9552 Time: 0.23\n",
      "Epoch: 84 Train Loss: 0.2493 Acc: 0.8796 Pre: 0.8819 Recall: 0.8767 F1: 0.8793 Train AUC: 0.9619 Val AUC: 0.9537 Val PRC: 0.9486 Time: 0.23\n",
      "Epoch: 85 Train Loss: 0.2589 Acc: 0.8777 Pre: 0.8614 Recall: 0.9002 F1: 0.8804 Train AUC: 0.9602 Val AUC: 0.9501 Val PRC: 0.9499 Time: 0.23\n",
      "Epoch: 86 Train Loss: 0.2524 Acc: 0.8924 Pre: 0.8893 Recall: 0.8963 F1: 0.8928 Train AUC: 0.9618 Val AUC: 0.9548 Val PRC: 0.9513 Time: 0.23\n",
      "Epoch: 87 Train Loss: 0.2565 Acc: 0.8855 Pre: 0.8745 Recall: 0.9002 F1: 0.8872 Train AUC: 0.9601 Val AUC: 0.9555 Val PRC: 0.9538 Time: 0.23\n",
      "Epoch: 88 Train Loss: 0.2595 Acc: 0.8836 Pre: 0.8813 Recall: 0.8865 F1: 0.8839 Train AUC: 0.9589 Val AUC: 0.9555 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 89 Train Loss: 0.2401 Acc: 0.8836 Pre: 0.8874 Recall: 0.8787 F1: 0.8830 Train AUC: 0.9649 Val AUC: 0.9546 Val PRC: 0.9514 Time: 0.23\n",
      "Epoch: 90 Train Loss: 0.2373 Acc: 0.8787 Pre: 0.8499 Recall: 0.9198 F1: 0.8835 Train AUC: 0.9652 Val AUC: 0.9560 Val PRC: 0.9544 Time: 0.23\n",
      "Epoch: 91 Train Loss: 0.2423 Acc: 0.8836 Pre: 0.8769 Recall: 0.8924 F1: 0.8846 Train AUC: 0.9646 Val AUC: 0.9557 Val PRC: 0.9575 Time: 0.23\n",
      "Epoch: 92 Train Loss: 0.2415 Acc: 0.8855 Pre: 0.8774 Recall: 0.8963 F1: 0.8867 Train AUC: 0.9651 Val AUC: 0.9572 Val PRC: 0.9577 Time: 0.23\n",
      "Epoch: 93 Train Loss: 0.2380 Acc: 0.8738 Pre: 0.8485 Recall: 0.9100 F1: 0.8782 Train AUC: 0.9654 Val AUC: 0.9502 Val PRC: 0.9489 Time: 0.23\n",
      "Epoch: 94 Train Loss: 0.2380 Acc: 0.8836 Pre: 0.8657 Recall: 0.9080 F1: 0.8863 Train AUC: 0.9661 Val AUC: 0.9530 Val PRC: 0.9491 Time: 0.23\n",
      "Epoch: 95 Train Loss: 0.2385 Acc: 0.8806 Pre: 0.8777 Recall: 0.8845 F1: 0.8811 Train AUC: 0.9658 Val AUC: 0.9524 Val PRC: 0.9542 Time: 0.23\n",
      "Epoch: 96 Train Loss: 0.2297 Acc: 0.8796 Pre: 0.8333 Recall: 0.9491 F1: 0.8875 Train AUC: 0.9678 Val AUC: 0.9575 Val PRC: 0.9545 Time: 0.24\n",
      "Epoch: 97 Train Loss: 0.2432 Acc: 0.8875 Pre: 0.8793 Recall: 0.8982 F1: 0.8887 Train AUC: 0.9635 Val AUC: 0.9531 Val PRC: 0.9474 Time: 0.23\n",
      "Epoch: 98 Train Loss: 0.2467 Acc: 0.8885 Pre: 0.8696 Recall: 0.9139 F1: 0.8912 Train AUC: 0.9630 Val AUC: 0.9575 Val PRC: 0.9556 Time: 0.23\n",
      "Epoch: 99 Train Loss: 0.2414 Acc: 0.8836 Pre: 0.8603 Recall: 0.9159 F1: 0.8872 Train AUC: 0.9652 Val AUC: 0.9557 Val PRC: 0.9522 Time: 0.23\n",
      "Epoch: 100 Train Loss: 0.2419 Acc: 0.8914 Pre: 0.8704 Recall: 0.9198 F1: 0.8944 Train AUC: 0.9648 Val AUC: 0.9572 Val PRC: 0.9538 Time: 0.24\n",
      "Epoch: 101 Train Loss: 0.2365 Acc: 0.8924 Pre: 0.8720 Recall: 0.9198 F1: 0.8952 Train AUC: 0.9672 Val AUC: 0.9589 Val PRC: 0.9536 Time: 0.24\n",
      "Epoch: 102 Train Loss: 0.2376 Acc: 0.8885 Pre: 0.8738 Recall: 0.9080 F1: 0.8906 Train AUC: 0.9669 Val AUC: 0.9546 Val PRC: 0.9513 Time: 0.23\n",
      "Epoch: 103 Train Loss: 0.2335 Acc: 0.8963 Pre: 0.8814 Recall: 0.9159 F1: 0.8983 Train AUC: 0.9686 Val AUC: 0.9578 Val PRC: 0.9532 Time: 0.23\n",
      "Epoch: 104 Train Loss: 0.2442 Acc: 0.8924 Pre: 0.8748 Recall: 0.9159 F1: 0.8948 Train AUC: 0.9650 Val AUC: 0.9590 Val PRC: 0.9603 Time: 0.23\n",
      "Epoch: 105 Train Loss: 0.2343 Acc: 0.8963 Pre: 0.8757 Recall: 0.9237 F1: 0.8990 Train AUC: 0.9667 Val AUC: 0.9582 Val PRC: 0.9547 Time: 0.24\n",
      "Epoch: 106 Train Loss: 0.2143 Acc: 0.8963 Pre: 0.8814 Recall: 0.9159 F1: 0.8983 Train AUC: 0.9723 Val AUC: 0.9600 Val PRC: 0.9539 Time: 0.23\n",
      "Epoch: 107 Train Loss: 0.2316 Acc: 0.8963 Pre: 0.8932 Recall: 0.9002 F1: 0.8967 Train AUC: 0.9673 Val AUC: 0.9614 Val PRC: 0.9569 Time: 0.23\n",
      "Epoch: 108 Train Loss: 0.2399 Acc: 0.8963 Pre: 0.8872 Recall: 0.9080 F1: 0.8975 Train AUC: 0.9647 Val AUC: 0.9587 Val PRC: 0.9543 Time: 0.23\n",
      "Epoch: 109 Train Loss: 0.2260 Acc: 0.8982 Pre: 0.9095 Recall: 0.8845 F1: 0.8968 Train AUC: 0.9694 Val AUC: 0.9623 Val PRC: 0.9640 Time: 0.23\n",
      "Epoch: 110 Train Loss: 0.2279 Acc: 0.9012 Pre: 0.9020 Recall: 0.9002 F1: 0.9011 Train AUC: 0.9686 Val AUC: 0.9594 Val PRC: 0.9539 Time: 0.23\n",
      "Epoch: 111 Train Loss: 0.2166 Acc: 0.8904 Pre: 0.8701 Recall: 0.9178 F1: 0.8933 Train AUC: 0.9716 Val AUC: 0.9574 Val PRC: 0.9598 Time: 0.23\n",
      "Epoch: 112 Train Loss: 0.2192 Acc: 0.8855 Pre: 0.8493 Recall: 0.9374 F1: 0.8912 Train AUC: 0.9709 Val AUC: 0.9583 Val PRC: 0.9535 Time: 0.23\n",
      "Epoch: 113 Train Loss: 0.2234 Acc: 0.8973 Pre: 0.8874 Recall: 0.9100 F1: 0.8986 Train AUC: 0.9701 Val AUC: 0.9569 Val PRC: 0.9524 Time: 0.23\n",
      "Epoch: 114 Train Loss: 0.2298 Acc: 0.9012 Pre: 0.8796 Recall: 0.9295 F1: 0.9039 Train AUC: 0.9683 Val AUC: 0.9610 Val PRC: 0.9604 Time: 0.28\n",
      "Epoch: 115 Train Loss: 0.2248 Acc: 0.8982 Pre: 0.8921 Recall: 0.9061 F1: 0.8990 Train AUC: 0.9699 Val AUC: 0.9596 Val PRC: 0.9594 Time: 0.23\n",
      "Epoch: 116 Train Loss: 0.2204 Acc: 0.8943 Pre: 0.8766 Recall: 0.9178 F1: 0.8967 Train AUC: 0.9710 Val AUC: 0.9601 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 117 Train Loss: 0.2264 Acc: 0.8943 Pre: 0.8752 Recall: 0.9198 F1: 0.8969 Train AUC: 0.9695 Val AUC: 0.9608 Val PRC: 0.9563 Time: 0.23\n",
      "Epoch: 118 Train Loss: 0.2266 Acc: 0.8982 Pre: 0.8876 Recall: 0.9119 F1: 0.8996 Train AUC: 0.9694 Val AUC: 0.9630 Val PRC: 0.9588 Time: 0.23\n",
      "Epoch: 119 Train Loss: 0.2208 Acc: 0.8992 Pre: 0.8878 Recall: 0.9139 F1: 0.9007 Train AUC: 0.9702 Val AUC: 0.9628 Val PRC: 0.9572 Time: 0.23\n",
      "Epoch: 120 Train Loss: 0.2231 Acc: 0.8924 Pre: 0.8706 Recall: 0.9217 F1: 0.8954 Train AUC: 0.9701 Val AUC: 0.9601 Val PRC: 0.9594 Time: 0.23\n",
      "Epoch: 121 Train Loss: 0.2084 Acc: 0.9022 Pre: 0.8870 Recall: 0.9217 F1: 0.9040 Train AUC: 0.9741 Val AUC: 0.9629 Val PRC: 0.9588 Time: 0.23\n",
      "Epoch: 122 Train Loss: 0.2145 Acc: 0.8982 Pre: 0.9078 Recall: 0.8865 F1: 0.8970 Train AUC: 0.9723 Val AUC: 0.9614 Val PRC: 0.9542 Time: 0.23\n",
      "Epoch: 123 Train Loss: 0.2151 Acc: 0.8953 Pre: 0.8840 Recall: 0.9100 F1: 0.8968 Train AUC: 0.9723 Val AUC: 0.9621 Val PRC: 0.9600 Time: 0.24\n",
      "Epoch: 124 Train Loss: 0.2168 Acc: 0.8992 Pre: 0.8984 Recall: 0.9002 F1: 0.8993 Train AUC: 0.9720 Val AUC: 0.9611 Val PRC: 0.9618 Time: 0.24\n",
      "Epoch: 125 Train Loss: 0.2102 Acc: 0.9002 Pre: 0.8822 Recall: 0.9237 F1: 0.9025 Train AUC: 0.9736 Val AUC: 0.9634 Val PRC: 0.9639 Time: 0.23\n",
      "Epoch: 126 Train Loss: 0.2029 Acc: 0.9012 Pre: 0.8973 Recall: 0.9061 F1: 0.9017 Train AUC: 0.9758 Val AUC: 0.9664 Val PRC: 0.9670 Time: 0.24\n",
      "Epoch: 127 Train Loss: 0.1982 Acc: 0.9031 Pre: 0.8902 Recall: 0.9198 F1: 0.9047 Train AUC: 0.9766 Val AUC: 0.9648 Val PRC: 0.9587 Time: 0.23\n",
      "Epoch: 128 Train Loss: 0.2174 Acc: 0.8992 Pre: 0.8835 Recall: 0.9198 F1: 0.9012 Train AUC: 0.9712 Val AUC: 0.9603 Val PRC: 0.9549 Time: 0.23\n",
      "Epoch: 129 Train Loss: 0.1998 Acc: 0.8904 Pre: 0.8743 Recall: 0.9119 F1: 0.8927 Train AUC: 0.9755 Val AUC: 0.9623 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 130 Train Loss: 0.2142 Acc: 0.8973 Pre: 0.8691 Recall: 0.9354 F1: 0.9010 Train AUC: 0.9725 Val AUC: 0.9621 Val PRC: 0.9623 Time: 0.23\n",
      "Epoch: 131 Train Loss: 0.2155 Acc: 0.9031 Pre: 0.8992 Recall: 0.9080 F1: 0.9036 Train AUC: 0.9719 Val AUC: 0.9633 Val PRC: 0.9631 Time: 0.24\n",
      "Epoch: 132 Train Loss: 0.2130 Acc: 0.8992 Pre: 0.8893 Recall: 0.9119 F1: 0.9005 Train AUC: 0.9726 Val AUC: 0.9635 Val PRC: 0.9635 Time: 0.24\n",
      "Epoch: 133 Train Loss: 0.2059 Acc: 0.8953 Pre: 0.8870 Recall: 0.9061 F1: 0.8964 Train AUC: 0.9739 Val AUC: 0.9600 Val PRC: 0.9552 Time: 0.24\n",
      "Epoch: 134 Train Loss: 0.2076 Acc: 0.9061 Pre: 0.9029 Recall: 0.9100 F1: 0.9064 Train AUC: 0.9733 Val AUC: 0.9611 Val PRC: 0.9533 Time: 0.24\n",
      "Epoch: 135 Train Loss: 0.2108 Acc: 0.8953 Pre: 0.8633 Recall: 0.9393 F1: 0.8997 Train AUC: 0.9722 Val AUC: 0.9608 Val PRC: 0.9574 Time: 0.23\n",
      "Epoch: 136 Train Loss: 0.2004 Acc: 0.9051 Pre: 0.8876 Recall: 0.9276 F1: 0.9072 Train AUC: 0.9754 Val AUC: 0.9622 Val PRC: 0.9565 Time: 0.23\n",
      "Epoch: 137 Train Loss: 0.2125 Acc: 0.9061 Pre: 0.9014 Recall: 0.9119 F1: 0.9066 Train AUC: 0.9718 Val AUC: 0.9655 Val PRC: 0.9654 Time: 0.23\n",
      "Epoch: 138 Train Loss: 0.1993 Acc: 0.9051 Pre: 0.8819 Recall: 0.9354 F1: 0.9079 Train AUC: 0.9764 Val AUC: 0.9651 Val PRC: 0.9604 Time: 0.23\n",
      "Epoch: 139 Train Loss: 0.2044 Acc: 0.9041 Pre: 0.8918 Recall: 0.9198 F1: 0.9056 Train AUC: 0.9738 Val AUC: 0.9615 Val PRC: 0.9597 Time: 0.23\n",
      "Epoch: 140 Train Loss: 0.1953 Acc: 0.9012 Pre: 0.8973 Recall: 0.9061 F1: 0.9017 Train AUC: 0.9771 Val AUC: 0.9643 Val PRC: 0.9593 Time: 0.23\n",
      "Epoch: 141 Train Loss: 0.2065 Acc: 0.8953 Pre: 0.8686 Recall: 0.9315 F1: 0.8990 Train AUC: 0.9739 Val AUC: 0.9628 Val PRC: 0.9553 Time: 0.24\n",
      "Epoch: 142 Train Loss: 0.1949 Acc: 0.8982 Pre: 0.8936 Recall: 0.9041 F1: 0.8988 Train AUC: 0.9783 Val AUC: 0.9644 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 143 Train Loss: 0.1920 Acc: 0.9051 Pre: 0.8805 Recall: 0.9374 F1: 0.9081 Train AUC: 0.9776 Val AUC: 0.9654 Val PRC: 0.9654 Time: 0.26\n",
      "Epoch: 144 Train Loss: 0.1928 Acc: 0.9022 Pre: 0.8663 Recall: 0.9511 F1: 0.9067 Train AUC: 0.9771 Val AUC: 0.9648 Val PRC: 0.9585 Time: 0.23\n",
      "Epoch: 145 Train Loss: 0.2051 Acc: 0.9002 Pre: 0.8940 Recall: 0.9080 F1: 0.9010 Train AUC: 0.9742 Val AUC: 0.9630 Val PRC: 0.9613 Time: 0.23\n",
      "Epoch: 146 Train Loss: 0.1994 Acc: 0.9178 Pre: 0.9067 Recall: 0.9315 F1: 0.9189 Train AUC: 0.9753 Val AUC: 0.9685 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 147 Train Loss: 0.2041 Acc: 0.9080 Pre: 0.8927 Recall: 0.9276 F1: 0.9098 Train AUC: 0.9747 Val AUC: 0.9670 Val PRC: 0.9622 Time: 0.24\n",
      "Epoch: 148 Train Loss: 0.1887 Acc: 0.9129 Pre: 0.8951 Recall: 0.9354 F1: 0.9148 Train AUC: 0.9789 Val AUC: 0.9693 Val PRC: 0.9658 Time: 0.23\n",
      "Epoch: 149 Train Loss: 0.1994 Acc: 0.9051 Pre: 0.8791 Recall: 0.9393 F1: 0.9082 Train AUC: 0.9760 Val AUC: 0.9658 Val PRC: 0.9656 Time: 0.23\n",
      "Epoch: 150 Train Loss: 0.2016 Acc: 0.9070 Pre: 0.8895 Recall: 0.9295 F1: 0.9091 Train AUC: 0.9752 Val AUC: 0.9666 Val PRC: 0.9647 Time: 0.23\n",
      "Epoch: 151 Train Loss: 0.1950 Acc: 0.9041 Pre: 0.8964 Recall: 0.9139 F1: 0.9050 Train AUC: 0.9763 Val AUC: 0.9645 Val PRC: 0.9580 Time: 0.23\n",
      "Epoch: 152 Train Loss: 0.1952 Acc: 0.9051 Pre: 0.8848 Recall: 0.9315 F1: 0.9075 Train AUC: 0.9773 Val AUC: 0.9659 Val PRC: 0.9600 Time: 0.24\n",
      "Epoch: 153 Train Loss: 0.2101 Acc: 0.9061 Pre: 0.9093 Recall: 0.9022 F1: 0.9057 Train AUC: 0.9727 Val AUC: 0.9663 Val PRC: 0.9662 Time: 0.23\n",
      "Epoch: 154 Train Loss: 0.2032 Acc: 0.9031 Pre: 0.9120 Recall: 0.8924 F1: 0.9021 Train AUC: 0.9740 Val AUC: 0.9646 Val PRC: 0.9640 Time: 0.23\n",
      "Epoch: 155 Train Loss: 0.1920 Acc: 0.8992 Pre: 0.8835 Recall: 0.9198 F1: 0.9012 Train AUC: 0.9773 Val AUC: 0.9595 Val PRC: 0.9570 Time: 0.23\n",
      "Epoch: 156 Train Loss: 0.2013 Acc: 0.8982 Pre: 0.8680 Recall: 0.9393 F1: 0.9023 Train AUC: 0.9748 Val AUC: 0.9624 Val PRC: 0.9608 Time: 0.23\n",
      "Epoch: 157 Train Loss: 0.2074 Acc: 0.9080 Pre: 0.9246 Recall: 0.8885 F1: 0.9062 Train AUC: 0.9729 Val AUC: 0.9671 Val PRC: 0.9645 Time: 0.23\n",
      "Epoch: 158 Train Loss: 0.1975 Acc: 0.9110 Pre: 0.9412 Recall: 0.8767 F1: 0.9078 Train AUC: 0.9750 Val AUC: 0.9672 Val PRC: 0.9658 Time: 0.23\n",
      "Epoch: 159 Train Loss: 0.1939 Acc: 0.9100 Pre: 0.9149 Recall: 0.9041 F1: 0.9094 Train AUC: 0.9764 Val AUC: 0.9662 Val PRC: 0.9668 Time: 0.23\n",
      "Epoch: 160 Train Loss: 0.1871 Acc: 0.9061 Pre: 0.8821 Recall: 0.9374 F1: 0.9089 Train AUC: 0.9778 Val AUC: 0.9685 Val PRC: 0.9666 Time: 0.40\n",
      "Epoch: 161 Train Loss: 0.1936 Acc: 0.9051 Pre: 0.8996 Recall: 0.9119 F1: 0.9057 Train AUC: 0.9764 Val AUC: 0.9668 Val PRC: 0.9661 Time: 0.23\n",
      "Epoch: 162 Train Loss: 0.1946 Acc: 0.8992 Pre: 0.8849 Recall: 0.9178 F1: 0.9011 Train AUC: 0.9759 Val AUC: 0.9646 Val PRC: 0.9634 Time: 0.23\n",
      "Epoch: 163 Train Loss: 0.1914 Acc: 0.9100 Pre: 0.9068 Recall: 0.9139 F1: 0.9103 Train AUC: 0.9772 Val AUC: 0.9662 Val PRC: 0.9597 Time: 0.24\n",
      "Epoch: 164 Train Loss: 0.1823 Acc: 0.9061 Pre: 0.9045 Recall: 0.9080 F1: 0.9062 Train AUC: 0.9789 Val AUC: 0.9684 Val PRC: 0.9662 Time: 0.23\n",
      "Epoch: 165 Train Loss: 0.1908 Acc: 0.9070 Pre: 0.8939 Recall: 0.9237 F1: 0.9086 Train AUC: 0.9764 Val AUC: 0.9679 Val PRC: 0.9653 Time: 0.23\n",
      "Epoch: 166 Train Loss: 0.1798 Acc: 0.9061 Pre: 0.9061 Recall: 0.9061 F1: 0.9061 Train AUC: 0.9799 Val AUC: 0.9664 Val PRC: 0.9649 Time: 0.23\n",
      "Epoch: 167 Train Loss: 0.1817 Acc: 0.9110 Pre: 0.9150 Recall: 0.9061 F1: 0.9105 Train AUC: 0.9793 Val AUC: 0.9691 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 168 Train Loss: 0.1803 Acc: 0.9129 Pre: 0.9220 Recall: 0.9022 F1: 0.9120 Train AUC: 0.9799 Val AUC: 0.9698 Val PRC: 0.9695 Time: 0.23\n",
      "Epoch: 169 Train Loss: 0.1957 Acc: 0.9031 Pre: 0.8946 Recall: 0.9139 F1: 0.9042 Train AUC: 0.9754 Val AUC: 0.9673 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 170 Train Loss: 0.1931 Acc: 0.9100 Pre: 0.8872 Recall: 0.9393 F1: 0.9125 Train AUC: 0.9767 Val AUC: 0.9697 Val PRC: 0.9695 Time: 0.24\n",
      "Epoch: 171 Train Loss: 0.1848 Acc: 0.9090 Pre: 0.8842 Recall: 0.9413 F1: 0.9118 Train AUC: 0.9780 Val AUC: 0.9691 Val PRC: 0.9663 Time: 0.23\n",
      "Epoch: 172 Train Loss: 0.1964 Acc: 0.9061 Pre: 0.8752 Recall: 0.9472 F1: 0.9098 Train AUC: 0.9758 Val AUC: 0.9695 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 173 Train Loss: 0.1865 Acc: 0.9139 Pre: 0.9013 Recall: 0.9295 F1: 0.9152 Train AUC: 0.9772 Val AUC: 0.9681 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 174 Train Loss: 0.1843 Acc: 0.9061 Pre: 0.8835 Recall: 0.9354 F1: 0.9087 Train AUC: 0.9789 Val AUC: 0.9666 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 175 Train Loss: 0.1788 Acc: 0.9061 Pre: 0.8983 Recall: 0.9159 F1: 0.9070 Train AUC: 0.9798 Val AUC: 0.9685 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 176 Train Loss: 0.1859 Acc: 0.9119 Pre: 0.9072 Recall: 0.9178 F1: 0.9125 Train AUC: 0.9776 Val AUC: 0.9714 Val PRC: 0.9714 Time: 0.23\n",
      "Epoch: 177 Train Loss: 0.1762 Acc: 0.9110 Pre: 0.9167 Recall: 0.9041 F1: 0.9103 Train AUC: 0.9808 Val AUC: 0.9708 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 178 Train Loss: 0.1816 Acc: 0.9080 Pre: 0.8927 Recall: 0.9276 F1: 0.9098 Train AUC: 0.9791 Val AUC: 0.9688 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 179 Train Loss: 0.1831 Acc: 0.9090 Pre: 0.9248 Recall: 0.8904 F1: 0.9073 Train AUC: 0.9787 Val AUC: 0.9688 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 180 Train Loss: 0.1688 Acc: 0.9139 Pre: 0.9139 Recall: 0.9139 F1: 0.9139 Train AUC: 0.9819 Val AUC: 0.9702 Val PRC: 0.9693 Time: 0.24\n",
      "Epoch: 181 Train Loss: 0.1816 Acc: 0.9139 Pre: 0.9172 Recall: 0.9100 F1: 0.9136 Train AUC: 0.9789 Val AUC: 0.9678 Val PRC: 0.9670 Time: 0.23\n",
      "Epoch: 182 Train Loss: 0.1895 Acc: 0.9119 Pre: 0.9010 Recall: 0.9256 F1: 0.9131 Train AUC: 0.9767 Val AUC: 0.9690 Val PRC: 0.9646 Time: 0.23\n",
      "Epoch: 183 Train Loss: 0.1788 Acc: 0.9129 Pre: 0.9289 Recall: 0.8943 F1: 0.9113 Train AUC: 0.9790 Val AUC: 0.9695 Val PRC: 0.9626 Time: 0.24\n",
      "Epoch: 184 Train Loss: 0.1655 Acc: 0.9139 Pre: 0.9290 Recall: 0.8963 F1: 0.9124 Train AUC: 0.9826 Val AUC: 0.9694 Val PRC: 0.9642 Time: 0.24\n",
      "Epoch: 185 Train Loss: 0.1669 Acc: 0.9119 Pre: 0.9040 Recall: 0.9217 F1: 0.9128 Train AUC: 0.9816 Val AUC: 0.9683 Val PRC: 0.9648 Time: 0.23\n",
      "Epoch: 186 Train Loss: 0.1746 Acc: 0.9070 Pre: 0.8824 Recall: 0.9393 F1: 0.9100 Train AUC: 0.9795 Val AUC: 0.9675 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 187 Train Loss: 0.1659 Acc: 0.9110 Pre: 0.8947 Recall: 0.9315 F1: 0.9128 Train AUC: 0.9825 Val AUC: 0.9671 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 188 Train Loss: 0.1715 Acc: 0.9119 Pre: 0.9152 Recall: 0.9080 F1: 0.9116 Train AUC: 0.9816 Val AUC: 0.9698 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 189 Train Loss: 0.1787 Acc: 0.9090 Pre: 0.9114 Recall: 0.9061 F1: 0.9087 Train AUC: 0.9795 Val AUC: 0.9672 Val PRC: 0.9652 Time: 0.24\n",
      "Epoch: 190 Train Loss: 0.1778 Acc: 0.9070 Pre: 0.8969 Recall: 0.9198 F1: 0.9082 Train AUC: 0.9796 Val AUC: 0.9688 Val PRC: 0.9677 Time: 0.24\n",
      "Epoch: 191 Train Loss: 0.1731 Acc: 0.9070 Pre: 0.8866 Recall: 0.9335 F1: 0.9094 Train AUC: 0.9808 Val AUC: 0.9687 Val PRC: 0.9685 Time: 0.23\n",
      "Epoch: 192 Train Loss: 0.1776 Acc: 0.9159 Pre: 0.9310 Recall: 0.8982 F1: 0.9143 Train AUC: 0.9806 Val AUC: 0.9726 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 193 Train Loss: 0.1651 Acc: 0.9110 Pre: 0.8962 Recall: 0.9295 F1: 0.9126 Train AUC: 0.9829 Val AUC: 0.9687 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 194 Train Loss: 0.1684 Acc: 0.9149 Pre: 0.9190 Recall: 0.9100 F1: 0.9145 Train AUC: 0.9819 Val AUC: 0.9707 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 195 Train Loss: 0.1608 Acc: 0.9080 Pre: 0.9112 Recall: 0.9041 F1: 0.9077 Train AUC: 0.9838 Val AUC: 0.9688 Val PRC: 0.9611 Time: 0.23\n",
      "Epoch: 196 Train Loss: 0.1785 Acc: 0.9119 Pre: 0.9136 Recall: 0.9100 F1: 0.9118 Train AUC: 0.9800 Val AUC: 0.9717 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 197 Train Loss: 0.1779 Acc: 0.9188 Pre: 0.9229 Recall: 0.9139 F1: 0.9184 Train AUC: 0.9783 Val AUC: 0.9713 Val PRC: 0.9679 Time: 0.23\n",
      "Epoch: 198 Train Loss: 0.1760 Acc: 0.9080 Pre: 0.8798 Recall: 0.9452 F1: 0.9113 Train AUC: 0.9790 Val AUC: 0.9682 Val PRC: 0.9659 Time: 0.24\n",
      "Epoch: 199 Train Loss: 0.1691 Acc: 0.9129 Pre: 0.8996 Recall: 0.9295 F1: 0.9143 Train AUC: 0.9811 Val AUC: 0.9711 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 200 Train Loss: 0.1729 Acc: 0.9129 Pre: 0.9237 Recall: 0.9002 F1: 0.9118 Train AUC: 0.9807 Val AUC: 0.9735 Val PRC: 0.9735 Time: 0.24\n",
      "Epoch: 201 Train Loss: 0.1674 Acc: 0.9178 Pre: 0.9366 Recall: 0.8963 F1: 0.9160 Train AUC: 0.9817 Val AUC: 0.9732 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 202 Train Loss: 0.1513 Acc: 0.9168 Pre: 0.9144 Recall: 0.9198 F1: 0.9171 Train AUC: 0.9851 Val AUC: 0.9709 Val PRC: 0.9665 Time: 0.23\n",
      "Epoch: 203 Train Loss: 0.1734 Acc: 0.9070 Pre: 0.8895 Recall: 0.9295 F1: 0.9091 Train AUC: 0.9800 Val AUC: 0.9653 Val PRC: 0.9612 Time: 0.23\n",
      "Epoch: 204 Train Loss: 0.1787 Acc: 0.9119 Pre: 0.9025 Recall: 0.9237 F1: 0.9130 Train AUC: 0.9788 Val AUC: 0.9686 Val PRC: 0.9650 Time: 0.23\n",
      "Epoch: 205 Train Loss: 0.1728 Acc: 0.9188 Pre: 0.9115 Recall: 0.9276 F1: 0.9195 Train AUC: 0.9815 Val AUC: 0.9725 Val PRC: 0.9694 Time: 0.23\n",
      "Epoch: 206 Train Loss: 0.1715 Acc: 0.9207 Pre: 0.9283 Recall: 0.9119 F1: 0.9200 Train AUC: 0.9808 Val AUC: 0.9696 Val PRC: 0.9665 Time: 0.24\n",
      "Epoch: 207 Train Loss: 0.1639 Acc: 0.9159 Pre: 0.9063 Recall: 0.9276 F1: 0.9168 Train AUC: 0.9824 Val AUC: 0.9718 Val PRC: 0.9703 Time: 0.24\n",
      "Epoch: 208 Train Loss: 0.1584 Acc: 0.9159 Pre: 0.8972 Recall: 0.9393 F1: 0.9178 Train AUC: 0.9834 Val AUC: 0.9696 Val PRC: 0.9681 Time: 0.24\n",
      "Epoch: 209 Train Loss: 0.1663 Acc: 0.9168 Pre: 0.9176 Recall: 0.9159 F1: 0.9167 Train AUC: 0.9819 Val AUC: 0.9703 Val PRC: 0.9597 Time: 0.23\n",
      "Epoch: 210 Train Loss: 0.1680 Acc: 0.9119 Pre: 0.9185 Recall: 0.9041 F1: 0.9112 Train AUC: 0.9808 Val AUC: 0.9720 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 211 Train Loss: 0.1583 Acc: 0.9149 Pre: 0.8926 Recall: 0.9432 F1: 0.9172 Train AUC: 0.9835 Val AUC: 0.9732 Val PRC: 0.9695 Time: 0.24\n",
      "Epoch: 212 Train Loss: 0.1679 Acc: 0.9139 Pre: 0.9107 Recall: 0.9178 F1: 0.9142 Train AUC: 0.9817 Val AUC: 0.9719 Val PRC: 0.9729 Time: 0.24\n",
      "Epoch: 213 Train Loss: 0.1663 Acc: 0.9227 Pre: 0.9235 Recall: 0.9217 F1: 0.9226 Train AUC: 0.9817 Val AUC: 0.9743 Val PRC: 0.9724 Time: 0.23\n",
      "Epoch: 214 Train Loss: 0.1658 Acc: 0.9178 Pre: 0.8903 Recall: 0.9530 F1: 0.9206 Train AUC: 0.9823 Val AUC: 0.9735 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 215 Train Loss: 0.1600 Acc: 0.9207 Pre: 0.9041 Recall: 0.9413 F1: 0.9223 Train AUC: 0.9850 Val AUC: 0.9743 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 216 Train Loss: 0.1462 Acc: 0.9188 Pre: 0.9131 Recall: 0.9256 F1: 0.9193 Train AUC: 0.9861 Val AUC: 0.9730 Val PRC: 0.9712 Time: 0.24\n",
      "Epoch: 217 Train Loss: 0.1601 Acc: 0.9168 Pre: 0.9004 Recall: 0.9374 F1: 0.9185 Train AUC: 0.9828 Val AUC: 0.9703 Val PRC: 0.9586 Time: 0.24\n",
      "Epoch: 218 Train Loss: 0.1667 Acc: 0.9198 Pre: 0.9055 Recall: 0.9374 F1: 0.9212 Train AUC: 0.9818 Val AUC: 0.9732 Val PRC: 0.9706 Time: 0.24\n",
      "Epoch: 219 Train Loss: 0.1549 Acc: 0.9159 Pre: 0.9017 Recall: 0.9335 F1: 0.9173 Train AUC: 0.9848 Val AUC: 0.9722 Val PRC: 0.9712 Time: 0.24\n",
      "Epoch: 220 Train Loss: 0.1509 Acc: 0.9168 Pre: 0.9004 Recall: 0.9374 F1: 0.9185 Train AUC: 0.9849 Val AUC: 0.9723 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 221 Train Loss: 0.1564 Acc: 0.9237 Pre: 0.9339 Recall: 0.9119 F1: 0.9228 Train AUC: 0.9844 Val AUC: 0.9754 Val PRC: 0.9726 Time: 0.23\n",
      "Epoch: 222 Train Loss: 0.1759 Acc: 0.9119 Pre: 0.8877 Recall: 0.9432 F1: 0.9146 Train AUC: 0.9790 Val AUC: 0.9725 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 223 Train Loss: 0.1579 Acc: 0.9217 Pre: 0.9301 Recall: 0.9119 F1: 0.9209 Train AUC: 0.9832 Val AUC: 0.9753 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 224 Train Loss: 0.1653 Acc: 0.9178 Pre: 0.9006 Recall: 0.9393 F1: 0.9195 Train AUC: 0.9832 Val AUC: 0.9726 Val PRC: 0.9702 Time: 0.23\n",
      "Epoch: 225 Train Loss: 0.1539 Acc: 0.9159 Pre: 0.8913 Recall: 0.9472 F1: 0.9184 Train AUC: 0.9843 Val AUC: 0.9734 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 226 Train Loss: 0.1440 Acc: 0.9149 Pre: 0.9030 Recall: 0.9295 F1: 0.9161 Train AUC: 0.9869 Val AUC: 0.9721 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 227 Train Loss: 0.1676 Acc: 0.9129 Pre: 0.9170 Recall: 0.9080 F1: 0.9125 Train AUC: 0.9812 Val AUC: 0.9703 Val PRC: 0.9695 Time: 0.24\n",
      "Epoch: 228 Train Loss: 0.1692 Acc: 0.9188 Pre: 0.8978 Recall: 0.9452 F1: 0.9209 Train AUC: 0.9822 Val AUC: 0.9725 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 229 Train Loss: 0.1722 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9808 Val AUC: 0.9737 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 230 Train Loss: 0.1558 Acc: 0.9188 Pre: 0.9115 Recall: 0.9276 F1: 0.9195 Train AUC: 0.9842 Val AUC: 0.9728 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 231 Train Loss: 0.1678 Acc: 0.9159 Pre: 0.9126 Recall: 0.9198 F1: 0.9162 Train AUC: 0.9810 Val AUC: 0.9732 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 232 Train Loss: 0.1689 Acc: 0.9168 Pre: 0.9080 Recall: 0.9276 F1: 0.9177 Train AUC: 0.9817 Val AUC: 0.9727 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 233 Train Loss: 0.1516 Acc: 0.9149 Pre: 0.9015 Recall: 0.9315 F1: 0.9163 Train AUC: 0.9856 Val AUC: 0.9742 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 234 Train Loss: 0.1469 Acc: 0.9227 Pre: 0.9138 Recall: 0.9335 F1: 0.9235 Train AUC: 0.9858 Val AUC: 0.9745 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 235 Train Loss: 0.1673 Acc: 0.9159 Pre: 0.9079 Recall: 0.9256 F1: 0.9167 Train AUC: 0.9809 Val AUC: 0.9691 Val PRC: 0.9606 Time: 0.23\n",
      "Epoch: 236 Train Loss: 0.1426 Acc: 0.9247 Pre: 0.9357 Recall: 0.9119 F1: 0.9237 Train AUC: 0.9860 Val AUC: 0.9732 Val PRC: 0.9677 Time: 0.24\n",
      "Epoch: 237 Train Loss: 0.1565 Acc: 0.9168 Pre: 0.9193 Recall: 0.9139 F1: 0.9166 Train AUC: 0.9837 Val AUC: 0.9720 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 238 Train Loss: 0.1474 Acc: 0.9168 Pre: 0.9019 Recall: 0.9354 F1: 0.9183 Train AUC: 0.9855 Val AUC: 0.9726 Val PRC: 0.9734 Time: 0.23\n",
      "Epoch: 239 Train Loss: 0.1463 Acc: 0.9207 Pre: 0.8981 Recall: 0.9491 F1: 0.9229 Train AUC: 0.9845 Val AUC: 0.9738 Val PRC: 0.9717 Time: 0.23\n",
      "Epoch: 240 Train Loss: 0.1555 Acc: 0.9198 Pre: 0.9149 Recall: 0.9256 F1: 0.9202 Train AUC: 0.9838 Val AUC: 0.9736 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 241 Train Loss: 0.1507 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9849 Val AUC: 0.9765 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 242 Train Loss: 0.1539 Acc: 0.9266 Pre: 0.9082 Recall: 0.9491 F1: 0.9282 Train AUC: 0.9836 Val AUC: 0.9772 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 243 Train Loss: 0.1611 Acc: 0.9266 Pre: 0.9325 Recall: 0.9198 F1: 0.9261 Train AUC: 0.9825 Val AUC: 0.9784 Val PRC: 0.9784 Time: 0.23\n",
      "Epoch: 244 Train Loss: 0.1445 Acc: 0.9247 Pre: 0.9465 Recall: 0.9002 F1: 0.9228 Train AUC: 0.9855 Val AUC: 0.9747 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 245 Train Loss: 0.1495 Acc: 0.9276 Pre: 0.9361 Recall: 0.9178 F1: 0.9269 Train AUC: 0.9850 Val AUC: 0.9768 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 246 Train Loss: 0.1433 Acc: 0.9217 Pre: 0.9058 Recall: 0.9413 F1: 0.9232 Train AUC: 0.9855 Val AUC: 0.9761 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 247 Train Loss: 0.1430 Acc: 0.9149 Pre: 0.8911 Recall: 0.9452 F1: 0.9174 Train AUC: 0.9864 Val AUC: 0.9742 Val PRC: 0.9734 Time: 0.23\n",
      "Epoch: 248 Train Loss: 0.1402 Acc: 0.9217 Pre: 0.9284 Recall: 0.9139 F1: 0.9211 Train AUC: 0.9861 Val AUC: 0.9721 Val PRC: 0.9710 Time: 0.23\n",
      "Epoch: 249 Train Loss: 0.1390 Acc: 0.9256 Pre: 0.9307 Recall: 0.9198 F1: 0.9252 Train AUC: 0.9859 Val AUC: 0.9749 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 250 Train Loss: 0.1352 Acc: 0.9237 Pre: 0.9124 Recall: 0.9374 F1: 0.9247 Train AUC: 0.9871 Val AUC: 0.9740 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 251 Train Loss: 0.1503 Acc: 0.9207 Pre: 0.9151 Recall: 0.9276 F1: 0.9213 Train AUC: 0.9843 Val AUC: 0.9745 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 252 Train Loss: 0.1477 Acc: 0.9188 Pre: 0.8993 Recall: 0.9432 F1: 0.9207 Train AUC: 0.9849 Val AUC: 0.9749 Val PRC: 0.9754 Time: 0.23\n",
      "Epoch: 253 Train Loss: 0.1468 Acc: 0.9256 Pre: 0.9175 Recall: 0.9354 F1: 0.9264 Train AUC: 0.9853 Val AUC: 0.9752 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 254 Train Loss: 0.1504 Acc: 0.9178 Pre: 0.8976 Recall: 0.9432 F1: 0.9198 Train AUC: 0.9844 Val AUC: 0.9719 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 255 Train Loss: 0.1480 Acc: 0.9227 Pre: 0.9045 Recall: 0.9452 F1: 0.9244 Train AUC: 0.9850 Val AUC: 0.9749 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 256 Train Loss: 0.1410 Acc: 0.9247 Pre: 0.9033 Recall: 0.9511 F1: 0.9266 Train AUC: 0.9865 Val AUC: 0.9755 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 257 Train Loss: 0.1640 Acc: 0.9227 Pre: 0.8985 Recall: 0.9530 F1: 0.9250 Train AUC: 0.9818 Val AUC: 0.9765 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 258 Train Loss: 0.1473 Acc: 0.9188 Pre: 0.8978 Recall: 0.9452 F1: 0.9209 Train AUC: 0.9847 Val AUC: 0.9736 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 259 Train Loss: 0.1430 Acc: 0.9276 Pre: 0.9310 Recall: 0.9237 F1: 0.9273 Train AUC: 0.9857 Val AUC: 0.9747 Val PRC: 0.9647 Time: 0.23\n",
      "Epoch: 260 Train Loss: 0.1433 Acc: 0.9247 Pre: 0.9173 Recall: 0.9335 F1: 0.9253 Train AUC: 0.9858 Val AUC: 0.9748 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 261 Train Loss: 0.1398 Acc: 0.9198 Pre: 0.9351 Recall: 0.9022 F1: 0.9183 Train AUC: 0.9865 Val AUC: 0.9751 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 262 Train Loss: 0.1470 Acc: 0.9178 Pre: 0.9331 Recall: 0.9002 F1: 0.9163 Train AUC: 0.9854 Val AUC: 0.9739 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 263 Train Loss: 0.1363 Acc: 0.9256 Pre: 0.9376 Recall: 0.9119 F1: 0.9246 Train AUC: 0.9873 Val AUC: 0.9749 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 264 Train Loss: 0.1425 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9855 Val AUC: 0.9753 Val PRC: 0.9724 Time: 0.23\n",
      "Epoch: 265 Train Loss: 0.1386 Acc: 0.9237 Pre: 0.8987 Recall: 0.9550 F1: 0.9260 Train AUC: 0.9866 Val AUC: 0.9753 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 266 Train Loss: 0.1479 Acc: 0.9237 Pre: 0.9155 Recall: 0.9335 F1: 0.9244 Train AUC: 0.9845 Val AUC: 0.9750 Val PRC: 0.9695 Time: 0.23\n",
      "Epoch: 267 Train Loss: 0.1398 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9864 Val AUC: 0.9750 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 268 Train Loss: 0.1408 Acc: 0.9207 Pre: 0.8996 Recall: 0.9472 F1: 0.9228 Train AUC: 0.9862 Val AUC: 0.9749 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 269 Train Loss: 0.1316 Acc: 0.9168 Pre: 0.9176 Recall: 0.9159 F1: 0.9167 Train AUC: 0.9878 Val AUC: 0.9739 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 270 Train Loss: 0.1299 Acc: 0.9178 Pre: 0.8932 Recall: 0.9491 F1: 0.9203 Train AUC: 0.9892 Val AUC: 0.9761 Val PRC: 0.9758 Time: 0.24\n",
      "Epoch: 271 Train Loss: 0.1352 Acc: 0.9237 Pre: 0.9155 Recall: 0.9335 F1: 0.9244 Train AUC: 0.9873 Val AUC: 0.9768 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 272 Train Loss: 0.1501 Acc: 0.9207 Pre: 0.8895 Recall: 0.9609 F1: 0.9238 Train AUC: 0.9856 Val AUC: 0.9760 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 273 Train Loss: 0.1407 Acc: 0.9198 Pre: 0.8980 Recall: 0.9472 F1: 0.9219 Train AUC: 0.9857 Val AUC: 0.9747 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 274 Train Loss: 0.1260 Acc: 0.9227 Pre: 0.9320 Recall: 0.9119 F1: 0.9219 Train AUC: 0.9895 Val AUC: 0.9735 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 275 Train Loss: 0.1280 Acc: 0.9227 Pre: 0.9186 Recall: 0.9276 F1: 0.9231 Train AUC: 0.9882 Val AUC: 0.9751 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 276 Train Loss: 0.1319 Acc: 0.9227 Pre: 0.9252 Recall: 0.9198 F1: 0.9225 Train AUC: 0.9871 Val AUC: 0.9764 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 277 Train Loss: 0.1391 Acc: 0.9266 Pre: 0.9291 Recall: 0.9237 F1: 0.9264 Train AUC: 0.9865 Val AUC: 0.9772 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 278 Train Loss: 0.1432 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9851 Val AUC: 0.9769 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 279 Train Loss: 0.1300 Acc: 0.9344 Pre: 0.9440 Recall: 0.9237 F1: 0.9337 Train AUC: 0.9878 Val AUC: 0.9770 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 280 Train Loss: 0.1339 Acc: 0.9247 Pre: 0.9110 Recall: 0.9413 F1: 0.9259 Train AUC: 0.9874 Val AUC: 0.9772 Val PRC: 0.9775 Time: 0.24\n",
      "Epoch: 281 Train Loss: 0.1320 Acc: 0.9276 Pre: 0.9396 Recall: 0.9139 F1: 0.9266 Train AUC: 0.9873 Val AUC: 0.9766 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 282 Train Loss: 0.1379 Acc: 0.9247 Pre: 0.9375 Recall: 0.9100 F1: 0.9235 Train AUC: 0.9859 Val AUC: 0.9760 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 283 Train Loss: 0.1220 Acc: 0.9188 Pre: 0.8934 Recall: 0.9511 F1: 0.9213 Train AUC: 0.9886 Val AUC: 0.9759 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 284 Train Loss: 0.1245 Acc: 0.9217 Pre: 0.9152 Recall: 0.9295 F1: 0.9223 Train AUC: 0.9889 Val AUC: 0.9748 Val PRC: 0.9705 Time: 0.23\n",
      "Epoch: 285 Train Loss: 0.1154 Acc: 0.9237 Pre: 0.9304 Recall: 0.9159 F1: 0.9231 Train AUC: 0.9903 Val AUC: 0.9770 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 286 Train Loss: 0.1274 Acc: 0.9237 Pre: 0.9093 Recall: 0.9413 F1: 0.9250 Train AUC: 0.9884 Val AUC: 0.9781 Val PRC: 0.9785 Time: 0.27\n",
      "Epoch: 287 Train Loss: 0.1179 Acc: 0.9217 Pre: 0.8983 Recall: 0.9511 F1: 0.9240 Train AUC: 0.9897 Val AUC: 0.9784 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 288 Train Loss: 0.1272 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9870 Val AUC: 0.9765 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 289 Train Loss: 0.1389 Acc: 0.9325 Pre: 0.9250 Recall: 0.9413 F1: 0.9331 Train AUC: 0.9843 Val AUC: 0.9781 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 290 Train Loss: 0.1284 Acc: 0.9247 Pre: 0.9033 Recall: 0.9511 F1: 0.9266 Train AUC: 0.9888 Val AUC: 0.9768 Val PRC: 0.9773 Time: 0.23\n",
      "Epoch: 291 Train Loss: 0.1421 Acc: 0.9286 Pre: 0.9212 Recall: 0.9374 F1: 0.9292 Train AUC: 0.9851 Val AUC: 0.9775 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 292 Train Loss: 0.1275 Acc: 0.9315 Pre: 0.9437 Recall: 0.9178 F1: 0.9306 Train AUC: 0.9874 Val AUC: 0.9774 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 293 Train Loss: 0.1120 Acc: 0.9256 Pre: 0.9081 Recall: 0.9472 F1: 0.9272 Train AUC: 0.9909 Val AUC: 0.9763 Val PRC: 0.9755 Time: 0.40\n",
      "Epoch: 294 Train Loss: 0.1253 Acc: 0.9237 Pre: 0.9108 Recall: 0.9393 F1: 0.9249 Train AUC: 0.9877 Val AUC: 0.9772 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 295 Train Loss: 0.1236 Acc: 0.9325 Pre: 0.9186 Recall: 0.9491 F1: 0.9336 Train AUC: 0.9882 Val AUC: 0.9764 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 296 Train Loss: 0.1300 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9874 Val AUC: 0.9785 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 297 Train Loss: 0.1304 Acc: 0.9295 Pre: 0.9134 Recall: 0.9491 F1: 0.9309 Train AUC: 0.9869 Val AUC: 0.9760 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 298 Train Loss: 0.1274 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9876 Val AUC: 0.9764 Val PRC: 0.9694 Time: 0.23\n",
      "Epoch: 299 Train Loss: 0.1124 Acc: 0.9237 Pre: 0.8958 Recall: 0.9589 F1: 0.9263 Train AUC: 0.9908 Val AUC: 0.9737 Val PRC: 0.9622 Time: 0.23\n",
      "Epoch: 300 Train Loss: 0.1198 Acc: 0.9266 Pre: 0.9176 Recall: 0.9374 F1: 0.9274 Train AUC: 0.9885 Val AUC: 0.9767 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 301 Train Loss: 0.1418 Acc: 0.9237 Pre: 0.9339 Recall: 0.9119 F1: 0.9228 Train AUC: 0.9844 Val AUC: 0.9765 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 302 Train Loss: 0.1272 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9880 Val AUC: 0.9763 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 303 Train Loss: 0.1256 Acc: 0.9276 Pre: 0.9344 Recall: 0.9198 F1: 0.9270 Train AUC: 0.9878 Val AUC: 0.9796 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 304 Train Loss: 0.1210 Acc: 0.9237 Pre: 0.9155 Recall: 0.9335 F1: 0.9244 Train AUC: 0.9888 Val AUC: 0.9769 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 305 Train Loss: 0.1272 Acc: 0.9198 Pre: 0.8865 Recall: 0.9628 F1: 0.9231 Train AUC: 0.9864 Val AUC: 0.9764 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 306 Train Loss: 0.1263 Acc: 0.9295 Pre: 0.9381 Recall: 0.9198 F1: 0.9289 Train AUC: 0.9874 Val AUC: 0.9780 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 307 Train Loss: 0.1170 Acc: 0.9295 Pre: 0.9295 Recall: 0.9295 F1: 0.9295 Train AUC: 0.9899 Val AUC: 0.9773 Val PRC: 0.9782 Time: 0.23\n",
      "Epoch: 308 Train Loss: 0.1290 Acc: 0.9237 Pre: 0.9287 Recall: 0.9178 F1: 0.9232 Train AUC: 0.9867 Val AUC: 0.9765 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 309 Train Loss: 0.1220 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9891 Val AUC: 0.9765 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 310 Train Loss: 0.1320 Acc: 0.9286 Pre: 0.9261 Recall: 0.9315 F1: 0.9288 Train AUC: 0.9869 Val AUC: 0.9771 Val PRC: 0.9751 Time: 0.24\n",
      "Epoch: 311 Train Loss: 0.1239 Acc: 0.9335 Pre: 0.9318 Recall: 0.9354 F1: 0.9336 Train AUC: 0.9882 Val AUC: 0.9794 Val PRC: 0.9788 Time: 0.24\n",
      "Epoch: 312 Train Loss: 0.1213 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9889 Val AUC: 0.9782 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 313 Train Loss: 0.1226 Acc: 0.9178 Pre: 0.9036 Recall: 0.9354 F1: 0.9192 Train AUC: 0.9887 Val AUC: 0.9718 Val PRC: 0.9641 Time: 0.23\n",
      "Epoch: 314 Train Loss: 0.1264 Acc: 0.9188 Pre: 0.9068 Recall: 0.9335 F1: 0.9200 Train AUC: 0.9874 Val AUC: 0.9734 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 315 Train Loss: 0.1328 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9863 Val AUC: 0.9757 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 316 Train Loss: 0.1339 Acc: 0.9227 Pre: 0.9219 Recall: 0.9237 F1: 0.9228 Train AUC: 0.9866 Val AUC: 0.9761 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 317 Train Loss: 0.1344 Acc: 0.9266 Pre: 0.9176 Recall: 0.9374 F1: 0.9274 Train AUC: 0.9907 Val AUC: 0.9749 Val PRC: 0.9661 Time: 0.23\n",
      "Epoch: 318 Train Loss: 0.1288 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9875 Val AUC: 0.9738 Val PRC: 0.9654 Time: 0.23\n",
      "Epoch: 319 Train Loss: 0.1152 Acc: 0.9237 Pre: 0.9237 Recall: 0.9237 F1: 0.9237 Train AUC: 0.9904 Val AUC: 0.9768 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 320 Train Loss: 0.1115 Acc: 0.9315 Pre: 0.9509 Recall: 0.9100 F1: 0.9300 Train AUC: 0.9901 Val AUC: 0.9783 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 321 Train Loss: 0.1130 Acc: 0.9227 Pre: 0.9390 Recall: 0.9041 F1: 0.9212 Train AUC: 0.9904 Val AUC: 0.9770 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 322 Train Loss: 0.1129 Acc: 0.9266 Pre: 0.9113 Recall: 0.9452 F1: 0.9280 Train AUC: 0.9897 Val AUC: 0.9754 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 323 Train Loss: 0.1190 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9889 Val AUC: 0.9760 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 324 Train Loss: 0.1233 Acc: 0.9247 Pre: 0.9004 Recall: 0.9550 F1: 0.9269 Train AUC: 0.9879 Val AUC: 0.9750 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 325 Train Loss: 0.1117 Acc: 0.9266 Pre: 0.9176 Recall: 0.9374 F1: 0.9274 Train AUC: 0.9906 Val AUC: 0.9760 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 326 Train Loss: 0.1332 Acc: 0.9286 Pre: 0.9212 Recall: 0.9374 F1: 0.9292 Train AUC: 0.9900 Val AUC: 0.9796 Val PRC: 0.9802 Time: 0.23\n",
      "Epoch: 327 Train Loss: 0.1207 Acc: 0.9276 Pre: 0.9130 Recall: 0.9452 F1: 0.9288 Train AUC: 0.9892 Val AUC: 0.9760 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 328 Train Loss: 0.1161 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9895 Val AUC: 0.9752 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 329 Train Loss: 0.1111 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9904 Val AUC: 0.9770 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 330 Train Loss: 0.1119 Acc: 0.9325 Pre: 0.9350 Recall: 0.9295 F1: 0.9323 Train AUC: 0.9894 Val AUC: 0.9760 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 331 Train Loss: 0.1124 Acc: 0.9247 Pre: 0.9173 Recall: 0.9335 F1: 0.9253 Train AUC: 0.9905 Val AUC: 0.9749 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 332 Train Loss: 0.1149 Acc: 0.9256 Pre: 0.9307 Recall: 0.9198 F1: 0.9252 Train AUC: 0.9883 Val AUC: 0.9716 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 333 Train Loss: 0.1159 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9896 Val AUC: 0.9732 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 334 Train Loss: 0.1056 Acc: 0.9266 Pre: 0.9343 Recall: 0.9178 F1: 0.9260 Train AUC: 0.9915 Val AUC: 0.9761 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 335 Train Loss: 0.1089 Acc: 0.9325 Pre: 0.9266 Recall: 0.9393 F1: 0.9329 Train AUC: 0.9902 Val AUC: 0.9757 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 336 Train Loss: 0.1242 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9879 Val AUC: 0.9755 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 337 Train Loss: 0.1133 Acc: 0.9305 Pre: 0.9365 Recall: 0.9237 F1: 0.9300 Train AUC: 0.9896 Val AUC: 0.9772 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 338 Train Loss: 0.1205 Acc: 0.9354 Pre: 0.9441 Recall: 0.9256 F1: 0.9348 Train AUC: 0.9888 Val AUC: 0.9787 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 339 Train Loss: 0.1135 Acc: 0.9335 Pre: 0.9386 Recall: 0.9276 F1: 0.9331 Train AUC: 0.9898 Val AUC: 0.9773 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 340 Train Loss: 0.1152 Acc: 0.9335 Pre: 0.9251 Recall: 0.9432 F1: 0.9341 Train AUC: 0.9889 Val AUC: 0.9767 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 341 Train Loss: 0.1014 Acc: 0.9354 Pre: 0.9238 Recall: 0.9491 F1: 0.9363 Train AUC: 0.9921 Val AUC: 0.9764 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 342 Train Loss: 0.0978 Acc: 0.9266 Pre: 0.9291 Recall: 0.9237 F1: 0.9264 Train AUC: 0.9928 Val AUC: 0.9735 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 343 Train Loss: 0.1116 Acc: 0.9325 Pre: 0.9402 Recall: 0.9237 F1: 0.9319 Train AUC: 0.9897 Val AUC: 0.9761 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 344 Train Loss: 0.1094 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9901 Val AUC: 0.9779 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 345 Train Loss: 0.0979 Acc: 0.9295 Pre: 0.9262 Recall: 0.9335 F1: 0.9298 Train AUC: 0.9927 Val AUC: 0.9761 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 346 Train Loss: 0.1079 Acc: 0.9295 Pre: 0.9213 Recall: 0.9393 F1: 0.9302 Train AUC: 0.9902 Val AUC: 0.9742 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 347 Train Loss: 0.1207 Acc: 0.9286 Pre: 0.9244 Recall: 0.9335 F1: 0.9289 Train AUC: 0.9880 Val AUC: 0.9762 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 348 Train Loss: 0.1079 Acc: 0.9227 Pre: 0.8985 Recall: 0.9530 F1: 0.9250 Train AUC: 0.9906 Val AUC: 0.9757 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 349 Train Loss: 0.1139 Acc: 0.9276 Pre: 0.9210 Recall: 0.9354 F1: 0.9282 Train AUC: 0.9878 Val AUC: 0.9735 Val PRC: 0.9713 Time: 0.24\n",
      "Epoch: 350 Train Loss: 0.0955 Acc: 0.9354 Pre: 0.9337 Recall: 0.9374 F1: 0.9355 Train AUC: 0.9923 Val AUC: 0.9798 Val PRC: 0.9809 Time: 0.23\n",
      "Epoch: 351 Train Loss: 0.0987 Acc: 0.9247 Pre: 0.9157 Recall: 0.9354 F1: 0.9255 Train AUC: 0.9924 Val AUC: 0.9748 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 352 Train Loss: 0.1142 Acc: 0.9276 Pre: 0.9243 Recall: 0.9315 F1: 0.9279 Train AUC: 0.9894 Val AUC: 0.9745 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 353 Train Loss: 0.1044 Acc: 0.9247 Pre: 0.9272 Recall: 0.9217 F1: 0.9244 Train AUC: 0.9915 Val AUC: 0.9722 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 354 Train Loss: 0.1015 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9916 Val AUC: 0.9759 Val PRC: 0.9770 Time: 0.24\n",
      "Epoch: 355 Train Loss: 0.1037 Acc: 0.9286 Pre: 0.9469 Recall: 0.9080 F1: 0.9271 Train AUC: 0.9919 Val AUC: 0.9749 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 356 Train Loss: 0.1121 Acc: 0.9335 Pre: 0.9203 Recall: 0.9491 F1: 0.9345 Train AUC: 0.9894 Val AUC: 0.9775 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 357 Train Loss: 0.1103 Acc: 0.9335 Pre: 0.9284 Recall: 0.9393 F1: 0.9339 Train AUC: 0.9896 Val AUC: 0.9757 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 358 Train Loss: 0.0998 Acc: 0.9325 Pre: 0.9492 Recall: 0.9139 F1: 0.9312 Train AUC: 0.9923 Val AUC: 0.9787 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 359 Train Loss: 0.1145 Acc: 0.9247 Pre: 0.9189 Recall: 0.9315 F1: 0.9252 Train AUC: 0.9876 Val AUC: 0.9740 Val PRC: 0.9686 Time: 0.23\n",
      "Epoch: 360 Train Loss: 0.1179 Acc: 0.9354 Pre: 0.9459 Recall: 0.9237 F1: 0.9347 Train AUC: 0.9885 Val AUC: 0.9779 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 361 Train Loss: 0.1017 Acc: 0.9286 Pre: 0.9345 Recall: 0.9217 F1: 0.9281 Train AUC: 0.9921 Val AUC: 0.9762 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 362 Train Loss: 0.1112 Acc: 0.9315 Pre: 0.9265 Recall: 0.9374 F1: 0.9319 Train AUC: 0.9886 Val AUC: 0.9764 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 363 Train Loss: 0.0983 Acc: 0.9276 Pre: 0.9639 Recall: 0.8885 F1: 0.9246 Train AUC: 0.9926 Val AUC: 0.9728 Val PRC: 0.9679 Time: 0.23\n",
      "Epoch: 364 Train Loss: 0.1091 Acc: 0.9247 Pre: 0.9272 Recall: 0.9217 F1: 0.9244 Train AUC: 0.9904 Val AUC: 0.9739 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 365 Train Loss: 0.1102 Acc: 0.9207 Pre: 0.9135 Recall: 0.9295 F1: 0.9214 Train AUC: 0.9902 Val AUC: 0.9711 Val PRC: 0.9673 Time: 0.23\n",
      "Epoch: 366 Train Loss: 0.1124 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9905 Val AUC: 0.9734 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 367 Train Loss: 0.1045 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9905 Val AUC: 0.9752 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 368 Train Loss: 0.1082 Acc: 0.9198 Pre: 0.9055 Recall: 0.9374 F1: 0.9212 Train AUC: 0.9913 Val AUC: 0.9722 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 369 Train Loss: 0.1161 Acc: 0.9198 Pre: 0.9040 Recall: 0.9393 F1: 0.9213 Train AUC: 0.9882 Val AUC: 0.9711 Val PRC: 0.9717 Time: 0.23\n",
      "Epoch: 370 Train Loss: 0.1000 Acc: 0.9276 Pre: 0.9414 Recall: 0.9119 F1: 0.9264 Train AUC: 0.9918 Val AUC: 0.9736 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 371 Train Loss: 0.1108 Acc: 0.9247 Pre: 0.9173 Recall: 0.9335 F1: 0.9253 Train AUC: 0.9901 Val AUC: 0.9699 Val PRC: 0.9655 Time: 0.23\n",
      "Epoch: 372 Train Loss: 0.1047 Acc: 0.9276 Pre: 0.9194 Recall: 0.9374 F1: 0.9283 Train AUC: 0.9909 Val AUC: 0.9742 Val PRC: 0.9753 Time: 0.24\n",
      "Epoch: 373 Train Loss: 0.1024 Acc: 0.9227 Pre: 0.9138 Recall: 0.9335 F1: 0.9235 Train AUC: 0.9921 Val AUC: 0.9750 Val PRC: 0.9753 Time: 0.27\n",
      "Epoch: 374 Train Loss: 0.1142 Acc: 0.9305 Pre: 0.9348 Recall: 0.9256 F1: 0.9302 Train AUC: 0.9896 Val AUC: 0.9734 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 375 Train Loss: 0.1165 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9887 Val AUC: 0.9709 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 376 Train Loss: 0.0971 Acc: 0.9305 Pre: 0.9231 Recall: 0.9393 F1: 0.9311 Train AUC: 0.9927 Val AUC: 0.9736 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 377 Train Loss: 0.1122 Acc: 0.9256 Pre: 0.9273 Recall: 0.9237 F1: 0.9255 Train AUC: 0.9896 Val AUC: 0.9753 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 378 Train Loss: 0.1089 Acc: 0.9276 Pre: 0.9293 Recall: 0.9256 F1: 0.9275 Train AUC: 0.9903 Val AUC: 0.9748 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 379 Train Loss: 0.1189 Acc: 0.9335 Pre: 0.9284 Recall: 0.9393 F1: 0.9339 Train AUC: 0.9880 Val AUC: 0.9767 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 380 Train Loss: 0.1058 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9912 Val AUC: 0.9784 Val PRC: 0.9801 Time: 0.23\n",
      "Epoch: 381 Train Loss: 0.1184 Acc: 0.9325 Pre: 0.9202 Recall: 0.9472 F1: 0.9335 Train AUC: 0.9887 Val AUC: 0.9756 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 382 Train Loss: 0.1026 Acc: 0.9354 Pre: 0.9441 Recall: 0.9256 F1: 0.9348 Train AUC: 0.9908 Val AUC: 0.9762 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 383 Train Loss: 0.1016 Acc: 0.9237 Pre: 0.9124 Recall: 0.9374 F1: 0.9247 Train AUC: 0.9915 Val AUC: 0.9736 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 384 Train Loss: 0.0973 Acc: 0.9364 Pre: 0.9442 Recall: 0.9276 F1: 0.9358 Train AUC: 0.9915 Val AUC: 0.9755 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 385 Train Loss: 0.1078 Acc: 0.9325 Pre: 0.9492 Recall: 0.9139 F1: 0.9312 Train AUC: 0.9903 Val AUC: 0.9737 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 386 Train Loss: 0.1079 Acc: 0.9295 Pre: 0.9381 Recall: 0.9198 F1: 0.9289 Train AUC: 0.9898 Val AUC: 0.9740 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 387 Train Loss: 0.1032 Acc: 0.9256 Pre: 0.9127 Recall: 0.9413 F1: 0.9268 Train AUC: 0.9916 Val AUC: 0.9778 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 388 Train Loss: 0.1013 Acc: 0.9247 Pre: 0.9064 Recall: 0.9472 F1: 0.9263 Train AUC: 0.9911 Val AUC: 0.9726 Val PRC: 0.9716 Time: 0.24\n",
      "Epoch: 389 Train Loss: 0.1061 Acc: 0.9295 Pre: 0.9165 Recall: 0.9452 F1: 0.9306 Train AUC: 0.9904 Val AUC: 0.9755 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 390 Train Loss: 0.1034 Acc: 0.9305 Pre: 0.9472 Recall: 0.9119 F1: 0.9292 Train AUC: 0.9909 Val AUC: 0.9745 Val PRC: 0.9736 Time: 0.24\n",
      "Epoch: 391 Train Loss: 0.1091 Acc: 0.9286 Pre: 0.9244 Recall: 0.9335 F1: 0.9289 Train AUC: 0.9894 Val AUC: 0.9730 Val PRC: 0.9663 Time: 0.24\n",
      "Epoch: 392 Train Loss: 0.0967 Acc: 0.9276 Pre: 0.9310 Recall: 0.9237 F1: 0.9273 Train AUC: 0.9927 Val AUC: 0.9757 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 393 Train Loss: 0.0978 Acc: 0.9286 Pre: 0.9212 Recall: 0.9374 F1: 0.9292 Train AUC: 0.9919 Val AUC: 0.9742 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 394 Train Loss: 0.0922 Acc: 0.9276 Pre: 0.9361 Recall: 0.9178 F1: 0.9269 Train AUC: 0.9919 Val AUC: 0.9734 Val PRC: 0.9670 Time: 0.23\n",
      "Epoch: 395 Train Loss: 0.0993 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9913 Val AUC: 0.9746 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 396 Train Loss: 0.0964 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9924 Val AUC: 0.9784 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 397 Train Loss: 0.1023 Acc: 0.9207 Pre: 0.8967 Recall: 0.9511 F1: 0.9231 Train AUC: 0.9905 Val AUC: 0.9734 Val PRC: 0.9632 Time: 0.24\n",
      "Epoch: 398 Train Loss: 0.0980 Acc: 0.9237 Pre: 0.9062 Recall: 0.9452 F1: 0.9253 Train AUC: 0.9909 Val AUC: 0.9744 Val PRC: 0.9610 Time: 0.24\n",
      "Epoch: 399 Train Loss: 0.0980 Acc: 0.9335 Pre: 0.9404 Recall: 0.9256 F1: 0.9329 Train AUC: 0.9905 Val AUC: 0.9789 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 400 Train Loss: 0.1056 Acc: 0.9266 Pre: 0.9022 Recall: 0.9569 F1: 0.9288 Train AUC: 0.9906 Val AUC: 0.9763 Val PRC: 0.9593 Time: 0.23\n",
      "Epoch: 401 Train Loss: 0.0885 Acc: 0.9315 Pre: 0.9349 Recall: 0.9276 F1: 0.9312 Train AUC: 0.9935 Val AUC: 0.9767 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 402 Train Loss: 0.1011 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9912 Val AUC: 0.9762 Val PRC: 0.9754 Time: 0.23\n",
      "Epoch: 403 Train Loss: 0.1151 Acc: 0.9295 Pre: 0.9312 Recall: 0.9276 F1: 0.9294 Train AUC: 0.9886 Val AUC: 0.9775 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 404 Train Loss: 0.1132 Acc: 0.9286 Pre: 0.9311 Recall: 0.9256 F1: 0.9284 Train AUC: 0.9882 Val AUC: 0.9788 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 405 Train Loss: 0.0972 Acc: 0.9335 Pre: 0.9352 Recall: 0.9315 F1: 0.9333 Train AUC: 0.9931 Val AUC: 0.9770 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 406 Train Loss: 0.0939 Acc: 0.9286 Pre: 0.9277 Recall: 0.9295 F1: 0.9286 Train AUC: 0.9935 Val AUC: 0.9766 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 407 Train Loss: 0.1190 Acc: 0.9325 Pre: 0.9218 Recall: 0.9452 F1: 0.9333 Train AUC: 0.9922 Val AUC: 0.9778 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 408 Train Loss: 0.0954 Acc: 0.9286 Pre: 0.9086 Recall: 0.9530 F1: 0.9303 Train AUC: 0.9924 Val AUC: 0.9770 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 409 Train Loss: 0.1011 Acc: 0.9305 Pre: 0.9314 Recall: 0.9295 F1: 0.9305 Train AUC: 0.9909 Val AUC: 0.9761 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 410 Train Loss: 0.0847 Acc: 0.9286 Pre: 0.9163 Recall: 0.9432 F1: 0.9296 Train AUC: 0.9943 Val AUC: 0.9750 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 411 Train Loss: 0.0930 Acc: 0.9237 Pre: 0.9077 Recall: 0.9432 F1: 0.9251 Train AUC: 0.9928 Val AUC: 0.9788 Val PRC: 0.9802 Time: 0.23\n",
      "Epoch: 412 Train Loss: 0.1222 Acc: 0.9247 Pre: 0.9306 Recall: 0.9178 F1: 0.9241 Train AUC: 0.9888 Val AUC: 0.9750 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 413 Train Loss: 0.1070 Acc: 0.9305 Pre: 0.9264 Recall: 0.9354 F1: 0.9309 Train AUC: 0.9909 Val AUC: 0.9755 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 414 Train Loss: 0.1015 Acc: 0.9276 Pre: 0.9344 Recall: 0.9198 F1: 0.9270 Train AUC: 0.9908 Val AUC: 0.9775 Val PRC: 0.9790 Time: 0.23\n",
      "Epoch: 415 Train Loss: 0.0884 Acc: 0.9266 Pre: 0.9082 Recall: 0.9491 F1: 0.9282 Train AUC: 0.9928 Val AUC: 0.9763 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 416 Train Loss: 0.0876 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9933 Val AUC: 0.9737 Val PRC: 0.9746 Time: 0.23\n",
      "Epoch: 417 Train Loss: 0.0990 Acc: 0.9295 Pre: 0.9381 Recall: 0.9198 F1: 0.9289 Train AUC: 0.9907 Val AUC: 0.9759 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 418 Train Loss: 0.0996 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9915 Val AUC: 0.9775 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 419 Train Loss: 0.0908 Acc: 0.9335 Pre: 0.9301 Recall: 0.9374 F1: 0.9337 Train AUC: 0.9926 Val AUC: 0.9791 Val PRC: 0.9800 Time: 0.23\n",
      "Epoch: 420 Train Loss: 0.1051 Acc: 0.9335 Pre: 0.9567 Recall: 0.9080 F1: 0.9317 Train AUC: 0.9899 Val AUC: 0.9759 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 421 Train Loss: 0.0987 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9914 Val AUC: 0.9760 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 422 Train Loss: 0.0994 Acc: 0.9315 Pre: 0.9315 Recall: 0.9315 F1: 0.9315 Train AUC: 0.9904 Val AUC: 0.9777 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 423 Train Loss: 0.0866 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9930 Val AUC: 0.9792 Val PRC: 0.9802 Time: 0.23\n",
      "Epoch: 424 Train Loss: 0.0977 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9901 Val AUC: 0.9761 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 425 Train Loss: 0.0947 Acc: 0.9335 Pre: 0.9251 Recall: 0.9432 F1: 0.9341 Train AUC: 0.9921 Val AUC: 0.9760 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 426 Train Loss: 0.0930 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9927 Val AUC: 0.9758 Val PRC: 0.9780 Time: 0.40\n",
      "Epoch: 427 Train Loss: 0.0896 Acc: 0.9266 Pre: 0.9504 Recall: 0.9002 F1: 0.9246 Train AUC: 0.9926 Val AUC: 0.9769 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 428 Train Loss: 0.0979 Acc: 0.9286 Pre: 0.9311 Recall: 0.9256 F1: 0.9284 Train AUC: 0.9912 Val AUC: 0.9760 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 429 Train Loss: 0.0830 Acc: 0.9335 Pre: 0.9369 Recall: 0.9295 F1: 0.9332 Train AUC: 0.9944 Val AUC: 0.9771 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 430 Train Loss: 0.0999 Acc: 0.9344 Pre: 0.9458 Recall: 0.9217 F1: 0.9336 Train AUC: 0.9922 Val AUC: 0.9763 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 431 Train Loss: 0.0829 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9941 Val AUC: 0.9749 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 432 Train Loss: 0.0831 Acc: 0.9364 Pre: 0.9514 Recall: 0.9198 F1: 0.9353 Train AUC: 0.9940 Val AUC: 0.9761 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 433 Train Loss: 0.0850 Acc: 0.9286 Pre: 0.9433 Recall: 0.9119 F1: 0.9274 Train AUC: 0.9935 Val AUC: 0.9755 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 434 Train Loss: 0.1065 Acc: 0.9344 Pre: 0.9458 Recall: 0.9217 F1: 0.9336 Train AUC: 0.9900 Val AUC: 0.9769 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 435 Train Loss: 0.1036 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9908 Val AUC: 0.9759 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 436 Train Loss: 0.1081 Acc: 0.9295 Pre: 0.9197 Recall: 0.9413 F1: 0.9304 Train AUC: 0.9901 Val AUC: 0.9759 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 437 Train Loss: 0.0860 Acc: 0.9286 Pre: 0.9212 Recall: 0.9374 F1: 0.9292 Train AUC: 0.9934 Val AUC: 0.9780 Val PRC: 0.9803 Time: 0.23\n",
      "Epoch: 438 Train Loss: 0.0932 Acc: 0.9256 Pre: 0.9324 Recall: 0.9178 F1: 0.9250 Train AUC: 0.9927 Val AUC: 0.9772 Val PRC: 0.9794 Time: 0.23\n",
      "Epoch: 439 Train Loss: 0.0944 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9913 Val AUC: 0.9761 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 440 Train Loss: 0.0906 Acc: 0.9335 Pre: 0.9493 Recall: 0.9159 F1: 0.9323 Train AUC: 0.9923 Val AUC: 0.9780 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 441 Train Loss: 0.0959 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9915 Val AUC: 0.9755 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 442 Train Loss: 0.0902 Acc: 0.9286 Pre: 0.9311 Recall: 0.9256 F1: 0.9284 Train AUC: 0.9932 Val AUC: 0.9779 Val PRC: 0.9810 Time: 0.23\n",
      "Epoch: 443 Train Loss: 0.0913 Acc: 0.9247 Pre: 0.8989 Recall: 0.9569 F1: 0.9270 Train AUC: 0.9929 Val AUC: 0.9757 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 444 Train Loss: 0.1238 Acc: 0.9256 Pre: 0.9430 Recall: 0.9061 F1: 0.9242 Train AUC: 0.9904 Val AUC: 0.9754 Val PRC: 0.9782 Time: 0.23\n",
      "Epoch: 445 Train Loss: 0.1036 Acc: 0.9325 Pre: 0.9402 Recall: 0.9237 F1: 0.9319 Train AUC: 0.9899 Val AUC: 0.9750 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 446 Train Loss: 0.0979 Acc: 0.9295 Pre: 0.9312 Recall: 0.9276 F1: 0.9294 Train AUC: 0.9910 Val AUC: 0.9751 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 447 Train Loss: 0.1011 Acc: 0.9227 Pre: 0.9186 Recall: 0.9276 F1: 0.9231 Train AUC: 0.9905 Val AUC: 0.9723 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 448 Train Loss: 0.0968 Acc: 0.9256 Pre: 0.9273 Recall: 0.9237 F1: 0.9255 Train AUC: 0.9913 Val AUC: 0.9717 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 449 Train Loss: 0.0940 Acc: 0.9286 Pre: 0.9228 Recall: 0.9354 F1: 0.9291 Train AUC: 0.9920 Val AUC: 0.9762 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 450 Train Loss: 0.0944 Acc: 0.9335 Pre: 0.9644 Recall: 0.9002 F1: 0.9312 Train AUC: 0.9908 Val AUC: 0.9741 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 451 Train Loss: 0.0972 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9914 Val AUC: 0.9762 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 452 Train Loss: 0.0954 Acc: 0.9217 Pre: 0.9152 Recall: 0.9295 F1: 0.9223 Train AUC: 0.9919 Val AUC: 0.9754 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 453 Train Loss: 0.1021 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9909 Val AUC: 0.9744 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 454 Train Loss: 0.0928 Acc: 0.9295 Pre: 0.9526 Recall: 0.9041 F1: 0.9277 Train AUC: 0.9914 Val AUC: 0.9758 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 455 Train Loss: 0.1094 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9872 Val AUC: 0.9711 Val PRC: 0.9597 Time: 0.23\n",
      "Epoch: 456 Train Loss: 0.1001 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9917 Val AUC: 0.9750 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 457 Train Loss: 0.0982 Acc: 0.9295 Pre: 0.9364 Recall: 0.9217 F1: 0.9290 Train AUC: 0.9924 Val AUC: 0.9761 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 458 Train Loss: 0.0902 Acc: 0.9295 Pre: 0.9416 Recall: 0.9159 F1: 0.9286 Train AUC: 0.9927 Val AUC: 0.9761 Val PRC: 0.9789 Time: 0.24\n",
      "Epoch: 459 Train Loss: 0.0926 Acc: 0.9227 Pre: 0.9320 Recall: 0.9119 F1: 0.9219 Train AUC: 0.9923 Val AUC: 0.9736 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 460 Train Loss: 0.0814 Acc: 0.9276 Pre: 0.9194 Recall: 0.9374 F1: 0.9283 Train AUC: 0.9942 Val AUC: 0.9738 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 461 Train Loss: 0.0959 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9920 Val AUC: 0.9737 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 462 Train Loss: 0.0853 Acc: 0.9305 Pre: 0.9215 Recall: 0.9413 F1: 0.9313 Train AUC: 0.9940 Val AUC: 0.9788 Val PRC: 0.9811 Time: 0.24\n",
      "Epoch: 463 Train Loss: 0.0923 Acc: 0.9393 Pre: 0.9499 Recall: 0.9276 F1: 0.9386 Train AUC: 0.9931 Val AUC: 0.9797 Val PRC: 0.9818 Time: 0.24\n",
      "Epoch: 464 Train Loss: 0.0929 Acc: 0.9335 Pre: 0.9686 Recall: 0.8963 F1: 0.9309 Train AUC: 0.9922 Val AUC: 0.9830 Val PRC: 0.9841 Time: 0.23\n",
      "Epoch: 465 Train Loss: 0.0850 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9934 Val AUC: 0.9784 Val PRC: 0.9789 Time: 0.24\n",
      "Epoch: 466 Train Loss: 0.0886 Acc: 0.9247 Pre: 0.9189 Recall: 0.9315 F1: 0.9252 Train AUC: 0.9930 Val AUC: 0.9732 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 467 Train Loss: 0.0962 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9919 Val AUC: 0.9774 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 468 Train Loss: 0.0947 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9918 Val AUC: 0.9757 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 469 Train Loss: 0.0926 Acc: 0.9344 Pre: 0.9405 Recall: 0.9276 F1: 0.9340 Train AUC: 0.9915 Val AUC: 0.9730 Val PRC: 0.9694 Time: 0.23\n",
      "Epoch: 470 Train Loss: 0.0855 Acc: 0.9247 Pre: 0.9094 Recall: 0.9432 F1: 0.9260 Train AUC: 0.9929 Val AUC: 0.9718 Val PRC: 0.9597 Time: 0.24\n",
      "Epoch: 471 Train Loss: 0.0852 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9935 Val AUC: 0.9731 Val PRC: 0.9688 Time: 0.24\n",
      "Epoch: 472 Train Loss: 0.0853 Acc: 0.9256 Pre: 0.9430 Recall: 0.9061 F1: 0.9242 Train AUC: 0.9924 Val AUC: 0.9749 Val PRC: 0.9732 Time: 0.24\n",
      "Epoch: 473 Train Loss: 0.0791 Acc: 0.9286 Pre: 0.9311 Recall: 0.9256 F1: 0.9284 Train AUC: 0.9943 Val AUC: 0.9745 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 474 Train Loss: 0.0978 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9908 Val AUC: 0.9764 Val PRC: 0.9767 Time: 0.24\n",
      "Epoch: 475 Train Loss: 0.0918 Acc: 0.9286 Pre: 0.9562 Recall: 0.8982 F1: 0.9263 Train AUC: 0.9912 Val AUC: 0.9752 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 476 Train Loss: 0.0753 Acc: 0.9256 Pre: 0.9223 Recall: 0.9295 F1: 0.9259 Train AUC: 0.9951 Val AUC: 0.9749 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 477 Train Loss: 0.0810 Acc: 0.9276 Pre: 0.9162 Recall: 0.9413 F1: 0.9286 Train AUC: 0.9939 Val AUC: 0.9766 Val PRC: 0.9774 Time: 0.24\n",
      "Epoch: 478 Train Loss: 0.0978 Acc: 0.9354 Pre: 0.9495 Recall: 0.9198 F1: 0.9344 Train AUC: 0.9919 Val AUC: 0.9763 Val PRC: 0.9777 Time: 0.24\n",
      "Epoch: 479 Train Loss: 0.0833 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9930 Val AUC: 0.9747 Val PRC: 0.9773 Time: 0.24\n",
      "Epoch: 480 Train Loss: 0.0845 Acc: 0.9295 Pre: 0.9197 Recall: 0.9413 F1: 0.9304 Train AUC: 0.9940 Val AUC: 0.9752 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 481 Train Loss: 0.0845 Acc: 0.9276 Pre: 0.9259 Recall: 0.9295 F1: 0.9277 Train AUC: 0.9932 Val AUC: 0.9742 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 482 Train Loss: 0.0808 Acc: 0.9286 Pre: 0.9195 Recall: 0.9393 F1: 0.9293 Train AUC: 0.9937 Val AUC: 0.9756 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 483 Train Loss: 0.0831 Acc: 0.9256 Pre: 0.9503 Recall: 0.8982 F1: 0.9235 Train AUC: 0.9942 Val AUC: 0.9761 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 484 Train Loss: 0.0883 Acc: 0.9354 Pre: 0.9550 Recall: 0.9139 F1: 0.9340 Train AUC: 0.9927 Val AUC: 0.9771 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 485 Train Loss: 0.0833 Acc: 0.9305 Pre: 0.9527 Recall: 0.9061 F1: 0.9288 Train AUC: 0.9933 Val AUC: 0.9754 Val PRC: 0.9768 Time: 0.23\n",
      "Epoch: 486 Train Loss: 0.0837 Acc: 0.9276 Pre: 0.9327 Recall: 0.9217 F1: 0.9272 Train AUC: 0.9929 Val AUC: 0.9742 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 487 Train Loss: 0.0799 Acc: 0.9315 Pre: 0.9419 Recall: 0.9198 F1: 0.9307 Train AUC: 0.9940 Val AUC: 0.9774 Val PRC: 0.9801 Time: 0.23\n",
      "Epoch: 488 Train Loss: 0.0954 Acc: 0.9315 Pre: 0.9455 Recall: 0.9159 F1: 0.9304 Train AUC: 0.9916 Val AUC: 0.9765 Val PRC: 0.9776 Time: 0.24\n",
      "Epoch: 489 Train Loss: 0.0839 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9941 Val AUC: 0.9772 Val PRC: 0.9805 Time: 0.23\n",
      "Epoch: 490 Train Loss: 0.0763 Acc: 0.9315 Pre: 0.9384 Recall: 0.9237 F1: 0.9310 Train AUC: 0.9948 Val AUC: 0.9767 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 491 Train Loss: 0.0782 Acc: 0.9286 Pre: 0.9328 Recall: 0.9237 F1: 0.9282 Train AUC: 0.9939 Val AUC: 0.9768 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 492 Train Loss: 0.0876 Acc: 0.9305 Pre: 0.9453 Recall: 0.9139 F1: 0.9294 Train AUC: 0.9923 Val AUC: 0.9756 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 493 Train Loss: 0.0812 Acc: 0.9286 Pre: 0.9363 Recall: 0.9198 F1: 0.9279 Train AUC: 0.9937 Val AUC: 0.9762 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 494 Train Loss: 0.0916 Acc: 0.9266 Pre: 0.9449 Recall: 0.9061 F1: 0.9251 Train AUC: 0.9907 Val AUC: 0.9732 Val PRC: 0.9697 Time: 0.23\n",
      "Epoch: 495 Train Loss: 0.1017 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9903 Val AUC: 0.9733 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 496 Train Loss: 0.0919 Acc: 0.9305 Pre: 0.9453 Recall: 0.9139 F1: 0.9294 Train AUC: 0.9925 Val AUC: 0.9748 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 497 Train Loss: 0.0848 Acc: 0.9295 Pre: 0.9279 Recall: 0.9315 F1: 0.9297 Train AUC: 0.9932 Val AUC: 0.9741 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 498 Train Loss: 0.0876 Acc: 0.9237 Pre: 0.9108 Recall: 0.9393 F1: 0.9249 Train AUC: 0.9933 Val AUC: 0.9762 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 499 Train Loss: 0.0897 Acc: 0.9256 Pre: 0.9290 Recall: 0.9217 F1: 0.9253 Train AUC: 0.9928 Val AUC: 0.9737 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 500 Train Loss: 0.1033 Acc: 0.9305 Pre: 0.9280 Recall: 0.9335 F1: 0.9307 Train AUC: 0.9915 Val AUC: 0.9704 Val PRC: 0.9728 Time: 0.24\n",
      "Fold: 2 Best Epoch: 464 Val acc: 0.9335 Val Pre: 0.9682 Val Recall: 0.8963 Val F1: 0.9309 Val AUC: 0.9830 Val PRC: 0.9841\n",
      "------this is 3th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.7389 Acc: 0.5020 Pre: 0.5010 Recall: 0.9980 F1: 0.6671 Train AUC: 0.4455 Val AUC: 0.4401 Val PRC: 0.4902 Time: 0.23\n",
      "Epoch: 2 Train Loss: 0.7642 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.3803 Val AUC: 0.3957 Val PRC: 0.4496 Time: 0.24\n",
      "Epoch: 3 Train Loss: 0.7003 Acc: 0.5450 Pre: 0.5253 Recall: 0.9354 F1: 0.6728 Train AUC: 0.5505 Val AUC: 0.5606 Val PRC: 0.5284 Time: 0.24\n",
      "Epoch: 4 Train Loss: 0.7328 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.4377 Val AUC: 0.4708 Val PRC: 0.4847 Time: 0.24\n",
      "Epoch: 5 Train Loss: 0.7120 Acc: 0.5029 Pre: 0.5015 Recall: 0.9980 F1: 0.6675 Train AUC: 0.5082 Val AUC: 0.5144 Val PRC: 0.5094 Time: 0.24\n",
      "Epoch: 6 Train Loss: 0.6956 Acc: 0.5157 Pre: 0.5080 Recall: 0.9902 F1: 0.6715 Train AUC: 0.5562 Val AUC: 0.5655 Val PRC: 0.5705 Time: 0.23\n",
      "Epoch: 7 Train Loss: 0.7123 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.5298 Val AUC: 0.4931 Val PRC: 0.5288 Time: 0.24\n",
      "Epoch: 8 Train Loss: 0.7226 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.4722 Val AUC: 0.4697 Val PRC: 0.5089 Time: 0.23\n",
      "Epoch: 9 Train Loss: 0.7216 Acc: 0.5010 Pre: 0.5005 Recall: 1.0000 F1: 0.6671 Train AUC: 0.4646 Val AUC: 0.4678 Val PRC: 0.4821 Time: 0.23\n",
      "Epoch: 10 Train Loss: 0.6727 Acc: 0.5274 Pre: 0.5144 Recall: 0.9804 F1: 0.6747 Train AUC: 0.6166 Val AUC: 0.6072 Val PRC: 0.6205 Time: 0.23\n",
      "Epoch: 11 Train Loss: 0.6761 Acc: 0.5362 Pre: 0.5194 Recall: 0.9687 F1: 0.6762 Train AUC: 0.6021 Val AUC: 0.5889 Val PRC: 0.5851 Time: 0.23\n",
      "Epoch: 12 Train Loss: 0.6751 Acc: 0.5665 Pre: 0.5374 Recall: 0.9550 F1: 0.6878 Train AUC: 0.6056 Val AUC: 0.5937 Val PRC: 0.5644 Time: 0.23\n",
      "Epoch: 13 Train Loss: 0.6559 Acc: 0.5303 Pre: 0.5163 Recall: 0.9589 F1: 0.6712 Train AUC: 0.6511 Val AUC: 0.6468 Val PRC: 0.6671 Time: 0.23\n",
      "Epoch: 14 Train Loss: 0.6633 Acc: 0.5499 Pre: 0.5274 Recall: 0.9609 F1: 0.6810 Train AUC: 0.6356 Val AUC: 0.6278 Val PRC: 0.6224 Time: 0.23\n",
      "Epoch: 15 Train Loss: 0.6477 Acc: 0.5636 Pre: 0.5348 Recall: 0.9765 F1: 0.6911 Train AUC: 0.6746 Val AUC: 0.6681 Val PRC: 0.6538 Time: 0.24\n",
      "Epoch: 16 Train Loss: 0.6544 Acc: 0.5812 Pre: 0.5466 Recall: 0.9530 F1: 0.6947 Train AUC: 0.6551 Val AUC: 0.6543 Val PRC: 0.6533 Time: 0.23\n",
      "Epoch: 17 Train Loss: 0.6257 Acc: 0.6605 Pre: 0.6057 Recall: 0.9198 F1: 0.7304 Train AUC: 0.7244 Val AUC: 0.7314 Val PRC: 0.7284 Time: 0.23\n",
      "Epoch: 18 Train Loss: 0.6164 Acc: 0.6341 Pre: 0.5826 Recall: 0.9452 F1: 0.7209 Train AUC: 0.7388 Val AUC: 0.7159 Val PRC: 0.7166 Time: 0.23\n",
      "Epoch: 19 Train Loss: 0.6054 Acc: 0.7074 Pre: 0.6391 Recall: 0.9530 F1: 0.7651 Train AUC: 0.7662 Val AUC: 0.7613 Val PRC: 0.7375 Time: 0.23\n",
      "Epoch: 20 Train Loss: 0.6041 Acc: 0.7339 Pre: 0.6639 Recall: 0.9472 F1: 0.7806 Train AUC: 0.7778 Val AUC: 0.7867 Val PRC: 0.7453 Time: 0.23\n",
      "Epoch: 21 Train Loss: 0.6000 Acc: 0.7241 Pre: 0.6517 Recall: 0.9628 F1: 0.7773 Train AUC: 0.7845 Val AUC: 0.7883 Val PRC: 0.7402 Time: 0.23\n",
      "Epoch: 22 Train Loss: 0.5808 Acc: 0.7740 Pre: 0.7115 Recall: 0.9217 F1: 0.8031 Train AUC: 0.8153 Val AUC: 0.8373 Val PRC: 0.7934 Time: 0.23\n",
      "Epoch: 23 Train Loss: 0.5783 Acc: 0.7436 Pre: 0.6717 Recall: 0.9530 F1: 0.7880 Train AUC: 0.8153 Val AUC: 0.8087 Val PRC: 0.7703 Time: 0.24\n",
      "Epoch: 24 Train Loss: 0.5782 Acc: 0.7564 Pre: 0.6882 Recall: 0.9374 F1: 0.7937 Train AUC: 0.8105 Val AUC: 0.8066 Val PRC: 0.7575 Time: 0.24\n",
      "Epoch: 25 Train Loss: 0.5430 Acc: 0.8023 Pre: 0.7764 Recall: 0.8493 F1: 0.8112 Train AUC: 0.8654 Val AUC: 0.8682 Val PRC: 0.8363 Time: 0.23\n",
      "Epoch: 26 Train Loss: 0.5537 Acc: 0.7769 Pre: 0.7141 Recall: 0.9237 F1: 0.8055 Train AUC: 0.8357 Val AUC: 0.8436 Val PRC: 0.8053 Time: 0.23\n",
      "Epoch: 27 Train Loss: 0.5063 Acc: 0.8297 Pre: 0.8337 Recall: 0.8239 F1: 0.8287 Train AUC: 0.8902 Val AUC: 0.9023 Val PRC: 0.8860 Time: 0.23\n",
      "Epoch: 28 Train Loss: 0.5005 Acc: 0.7994 Pre: 0.7576 Recall: 0.8806 F1: 0.8145 Train AUC: 0.8906 Val AUC: 0.8876 Val PRC: 0.8805 Time: 0.23\n",
      "Epoch: 29 Train Loss: 0.4963 Acc: 0.8072 Pre: 0.7661 Recall: 0.8845 F1: 0.8211 Train AUC: 0.8848 Val AUC: 0.8977 Val PRC: 0.8864 Time: 0.23\n",
      "Epoch: 30 Train Loss: 0.4787 Acc: 0.8337 Pre: 0.8417 Recall: 0.8219 F1: 0.8317 Train AUC: 0.8967 Val AUC: 0.9143 Val PRC: 0.9089 Time: 0.23\n",
      "Epoch: 31 Train Loss: 0.4792 Acc: 0.7838 Pre: 0.7316 Recall: 0.8963 F1: 0.8056 Train AUC: 0.8863 Val AUC: 0.8934 Val PRC: 0.9006 Time: 0.23\n",
      "Epoch: 32 Train Loss: 0.4583 Acc: 0.8434 Pre: 0.8462 Recall: 0.8395 F1: 0.8428 Train AUC: 0.8988 Val AUC: 0.9086 Val PRC: 0.9119 Time: 0.24\n",
      "Epoch: 33 Train Loss: 0.4352 Acc: 0.8268 Pre: 0.8465 Recall: 0.7984 F1: 0.8218 Train AUC: 0.9002 Val AUC: 0.9117 Val PRC: 0.9210 Time: 0.23\n",
      "Epoch: 34 Train Loss: 0.4404 Acc: 0.8258 Pre: 0.8447 Recall: 0.7984 F1: 0.8209 Train AUC: 0.8928 Val AUC: 0.9046 Val PRC: 0.9134 Time: 0.23\n",
      "Epoch: 35 Train Loss: 0.4275 Acc: 0.8200 Pre: 0.8162 Recall: 0.8258 F1: 0.8210 Train AUC: 0.8972 Val AUC: 0.9079 Val PRC: 0.9108 Time: 0.23\n",
      "Epoch: 36 Train Loss: 0.4137 Acc: 0.8376 Pre: 0.8859 Recall: 0.7750 F1: 0.8267 Train AUC: 0.9021 Val AUC: 0.9121 Val PRC: 0.9251 Time: 0.23\n",
      "Epoch: 37 Train Loss: 0.3940 Acc: 0.8366 Pre: 0.8675 Recall: 0.7945 F1: 0.8294 Train AUC: 0.9080 Val AUC: 0.9175 Val PRC: 0.9267 Time: 0.23\n",
      "Epoch: 38 Train Loss: 0.3938 Acc: 0.8376 Pre: 0.8947 Recall: 0.7652 F1: 0.8249 Train AUC: 0.9087 Val AUC: 0.9124 Val PRC: 0.9194 Time: 0.24\n",
      "Epoch: 39 Train Loss: 0.3772 Acc: 0.8483 Pre: 0.8739 Recall: 0.8141 F1: 0.8430 Train AUC: 0.9147 Val AUC: 0.9224 Val PRC: 0.9247 Time: 0.24\n",
      "Epoch: 40 Train Loss: 0.3838 Acc: 0.8337 Pre: 0.8635 Recall: 0.7926 F1: 0.8265 Train AUC: 0.9063 Val AUC: 0.9150 Val PRC: 0.9203 Time: 0.24\n",
      "Epoch: 41 Train Loss: 0.3590 Acc: 0.8346 Pre: 0.8379 Recall: 0.8297 F1: 0.8338 Train AUC: 0.9215 Val AUC: 0.9240 Val PRC: 0.9286 Time: 0.24\n",
      "Epoch: 42 Train Loss: 0.3590 Acc: 0.8464 Pre: 0.8899 Recall: 0.7906 F1: 0.8373 Train AUC: 0.9161 Val AUC: 0.9235 Val PRC: 0.9312 Time: 0.24\n",
      "Epoch: 43 Train Loss: 0.3499 Acc: 0.8532 Pre: 0.8546 Recall: 0.8513 F1: 0.8529 Train AUC: 0.9254 Val AUC: 0.9330 Val PRC: 0.9377 Time: 0.24\n",
      "Epoch: 44 Train Loss: 0.3467 Acc: 0.8395 Pre: 0.8343 Recall: 0.8474 F1: 0.8408 Train AUC: 0.9253 Val AUC: 0.9314 Val PRC: 0.9353 Time: 0.23\n",
      "Epoch: 45 Train Loss: 0.3472 Acc: 0.8483 Pre: 0.8371 Recall: 0.8650 F1: 0.8508 Train AUC: 0.9261 Val AUC: 0.9325 Val PRC: 0.9361 Time: 0.23\n",
      "Epoch: 46 Train Loss: 0.3279 Acc: 0.8513 Pre: 0.8554 Recall: 0.8454 F1: 0.8504 Train AUC: 0.9328 Val AUC: 0.9382 Val PRC: 0.9380 Time: 0.23\n",
      "Epoch: 47 Train Loss: 0.3328 Acc: 0.8376 Pre: 0.8142 Recall: 0.8748 F1: 0.8434 Train AUC: 0.9293 Val AUC: 0.9331 Val PRC: 0.9399 Time: 0.24\n",
      "Epoch: 48 Train Loss: 0.3340 Acc: 0.8571 Pre: 0.9176 Recall: 0.7847 F1: 0.8460 Train AUC: 0.9265 Val AUC: 0.9355 Val PRC: 0.9429 Time: 0.23\n",
      "Epoch: 49 Train Loss: 0.3345 Acc: 0.8640 Pre: 0.8991 Recall: 0.8200 F1: 0.8577 Train AUC: 0.9262 Val AUC: 0.9341 Val PRC: 0.9413 Time: 0.23\n",
      "Epoch: 50 Train Loss: 0.3120 Acc: 0.8659 Pre: 0.8569 Recall: 0.8787 F1: 0.8676 Train AUC: 0.9374 Val AUC: 0.9429 Val PRC: 0.9460 Time: 0.23\n",
      "Epoch: 51 Train Loss: 0.3262 Acc: 0.8552 Pre: 0.8608 Recall: 0.8474 F1: 0.8540 Train AUC: 0.9311 Val AUC: 0.9335 Val PRC: 0.9375 Time: 0.24\n",
      "Epoch: 52 Train Loss: 0.3166 Acc: 0.8611 Pre: 0.9002 Recall: 0.8121 F1: 0.8539 Train AUC: 0.9382 Val AUC: 0.9338 Val PRC: 0.9383 Time: 0.24\n",
      "Epoch: 53 Train Loss: 0.3097 Acc: 0.8650 Pre: 0.8539 Recall: 0.8806 F1: 0.8671 Train AUC: 0.9411 Val AUC: 0.9438 Val PRC: 0.9459 Time: 0.24\n",
      "Epoch: 54 Train Loss: 0.3064 Acc: 0.8493 Pre: 0.8240 Recall: 0.8885 F1: 0.8550 Train AUC: 0.9401 Val AUC: 0.9369 Val PRC: 0.9408 Time: 0.23\n",
      "Epoch: 55 Train Loss: 0.2992 Acc: 0.8513 Pre: 0.8270 Recall: 0.8885 F1: 0.8566 Train AUC: 0.9433 Val AUC: 0.9405 Val PRC: 0.9448 Time: 0.23\n",
      "Epoch: 56 Train Loss: 0.3159 Acc: 0.8728 Pre: 0.8944 Recall: 0.8454 F1: 0.8692 Train AUC: 0.9366 Val AUC: 0.9396 Val PRC: 0.9434 Time: 0.40\n",
      "Epoch: 57 Train Loss: 0.3056 Acc: 0.8611 Pre: 0.8985 Recall: 0.8141 F1: 0.8542 Train AUC: 0.9393 Val AUC: 0.9397 Val PRC: 0.9466 Time: 0.24\n",
      "Epoch: 58 Train Loss: 0.3046 Acc: 0.8542 Pre: 0.8415 Recall: 0.8728 F1: 0.8569 Train AUC: 0.9418 Val AUC: 0.9408 Val PRC: 0.9465 Time: 0.24\n",
      "Epoch: 59 Train Loss: 0.2904 Acc: 0.8552 Pre: 0.8306 Recall: 0.8924 F1: 0.8604 Train AUC: 0.9458 Val AUC: 0.9433 Val PRC: 0.9451 Time: 0.24\n",
      "Epoch: 60 Train Loss: 0.3008 Acc: 0.8836 Pre: 0.8984 Recall: 0.8650 F1: 0.8814 Train AUC: 0.9443 Val AUC: 0.9489 Val PRC: 0.9528 Time: 0.24\n",
      "Epoch: 61 Train Loss: 0.3015 Acc: 0.8738 Pre: 0.8979 Recall: 0.8434 F1: 0.8698 Train AUC: 0.9428 Val AUC: 0.9479 Val PRC: 0.9501 Time: 0.24\n",
      "Epoch: 62 Train Loss: 0.2912 Acc: 0.8679 Pre: 0.8933 Recall: 0.8356 F1: 0.8635 Train AUC: 0.9460 Val AUC: 0.9429 Val PRC: 0.9493 Time: 0.24\n",
      "Epoch: 63 Train Loss: 0.2787 Acc: 0.8738 Pre: 0.8914 Recall: 0.8513 F1: 0.8709 Train AUC: 0.9501 Val AUC: 0.9450 Val PRC: 0.9502 Time: 0.23\n",
      "Epoch: 64 Train Loss: 0.2872 Acc: 0.8826 Pre: 0.8982 Recall: 0.8630 F1: 0.8802 Train AUC: 0.9487 Val AUC: 0.9506 Val PRC: 0.9534 Time: 0.23\n",
      "Epoch: 65 Train Loss: 0.2848 Acc: 0.8826 Pre: 0.9278 Recall: 0.8297 F1: 0.8760 Train AUC: 0.9478 Val AUC: 0.9469 Val PRC: 0.9459 Time: 0.23\n",
      "Epoch: 66 Train Loss: 0.2910 Acc: 0.8767 Pre: 0.9231 Recall: 0.8219 F1: 0.8696 Train AUC: 0.9450 Val AUC: 0.9465 Val PRC: 0.9506 Time: 0.23\n",
      "Epoch: 67 Train Loss: 0.2928 Acc: 0.8777 Pre: 0.8845 Recall: 0.8689 F1: 0.8766 Train AUC: 0.9459 Val AUC: 0.9502 Val PRC: 0.9543 Time: 0.24\n",
      "Epoch: 68 Train Loss: 0.2841 Acc: 0.8708 Pre: 0.8555 Recall: 0.8924 F1: 0.8736 Train AUC: 0.9489 Val AUC: 0.9521 Val PRC: 0.9554 Time: 0.24\n",
      "Epoch: 69 Train Loss: 0.2771 Acc: 0.8777 Pre: 0.9004 Recall: 0.8493 F1: 0.8741 Train AUC: 0.9512 Val AUC: 0.9534 Val PRC: 0.9548 Time: 0.24\n",
      "Epoch: 70 Train Loss: 0.2663 Acc: 0.8699 Pre: 0.8677 Recall: 0.8728 F1: 0.8702 Train AUC: 0.9549 Val AUC: 0.9502 Val PRC: 0.9539 Time: 0.23\n",
      "Epoch: 71 Train Loss: 0.2788 Acc: 0.8708 Pre: 0.8610 Recall: 0.8845 F1: 0.8726 Train AUC: 0.9516 Val AUC: 0.9475 Val PRC: 0.9484 Time: 0.23\n",
      "Epoch: 72 Train Loss: 0.2688 Acc: 0.8757 Pre: 0.8516 Recall: 0.9100 F1: 0.8798 Train AUC: 0.9555 Val AUC: 0.9542 Val PRC: 0.9562 Time: 0.23\n",
      "Epoch: 73 Train Loss: 0.2583 Acc: 0.8748 Pre: 0.8822 Recall: 0.8650 F1: 0.8735 Train AUC: 0.9569 Val AUC: 0.9509 Val PRC: 0.9538 Time: 0.23\n",
      "Epoch: 74 Train Loss: 0.2875 Acc: 0.8757 Pre: 0.8556 Recall: 0.9041 F1: 0.8792 Train AUC: 0.9485 Val AUC: 0.9529 Val PRC: 0.9541 Time: 0.23\n",
      "Epoch: 75 Train Loss: 0.2760 Acc: 0.8796 Pre: 0.8688 Recall: 0.8943 F1: 0.8814 Train AUC: 0.9524 Val AUC: 0.9557 Val PRC: 0.9576 Time: 0.23\n",
      "Epoch: 76 Train Loss: 0.2660 Acc: 0.8787 Pre: 0.8644 Recall: 0.8982 F1: 0.8810 Train AUC: 0.9547 Val AUC: 0.9564 Val PRC: 0.9568 Time: 0.23\n",
      "Epoch: 77 Train Loss: 0.2623 Acc: 0.8787 Pre: 0.8550 Recall: 0.9119 F1: 0.8826 Train AUC: 0.9568 Val AUC: 0.9534 Val PRC: 0.9497 Time: 0.23\n",
      "Epoch: 78 Train Loss: 0.2627 Acc: 0.8904 Pre: 0.9030 Recall: 0.8748 F1: 0.8887 Train AUC: 0.9568 Val AUC: 0.9581 Val PRC: 0.9596 Time: 0.23\n",
      "Epoch: 79 Train Loss: 0.2692 Acc: 0.8924 Pre: 0.8955 Recall: 0.8885 F1: 0.8919 Train AUC: 0.9549 Val AUC: 0.9617 Val PRC: 0.9612 Time: 0.25\n",
      "Epoch: 80 Train Loss: 0.2616 Acc: 0.8806 Pre: 0.8994 Recall: 0.8571 F1: 0.8778 Train AUC: 0.9569 Val AUC: 0.9556 Val PRC: 0.9536 Time: 0.23\n",
      "Epoch: 81 Train Loss: 0.2545 Acc: 0.8738 Pre: 0.8498 Recall: 0.9080 F1: 0.8780 Train AUC: 0.9588 Val AUC: 0.9564 Val PRC: 0.9538 Time: 0.23\n",
      "Epoch: 82 Train Loss: 0.2574 Acc: 0.8836 Pre: 0.8889 Recall: 0.8767 F1: 0.8828 Train AUC: 0.9581 Val AUC: 0.9571 Val PRC: 0.9572 Time: 0.23\n",
      "Epoch: 83 Train Loss: 0.2556 Acc: 0.8816 Pre: 0.8625 Recall: 0.9080 F1: 0.8847 Train AUC: 0.9597 Val AUC: 0.9591 Val PRC: 0.9543 Time: 0.23\n",
      "Epoch: 84 Train Loss: 0.2550 Acc: 0.8806 Pre: 0.8851 Recall: 0.8748 F1: 0.8799 Train AUC: 0.9596 Val AUC: 0.9558 Val PRC: 0.9535 Time: 0.23\n",
      "Epoch: 85 Train Loss: 0.2533 Acc: 0.8855 Pre: 0.8518 Recall: 0.9335 F1: 0.8908 Train AUC: 0.9611 Val AUC: 0.9589 Val PRC: 0.9556 Time: 0.23\n",
      "Epoch: 86 Train Loss: 0.2451 Acc: 0.8885 Pre: 0.8994 Recall: 0.8748 F1: 0.8869 Train AUC: 0.9629 Val AUC: 0.9597 Val PRC: 0.9543 Time: 0.23\n",
      "Epoch: 87 Train Loss: 0.2426 Acc: 0.8865 Pre: 0.8546 Recall: 0.9315 F1: 0.8914 Train AUC: 0.9638 Val AUC: 0.9596 Val PRC: 0.9576 Time: 0.24\n",
      "Epoch: 88 Train Loss: 0.2659 Acc: 0.8836 Pre: 0.8858 Recall: 0.8806 F1: 0.8832 Train AUC: 0.9557 Val AUC: 0.9597 Val PRC: 0.9550 Time: 0.24\n",
      "Epoch: 89 Train Loss: 0.2537 Acc: 0.8845 Pre: 0.8816 Recall: 0.8885 F1: 0.8850 Train AUC: 0.9600 Val AUC: 0.9572 Val PRC: 0.9530 Time: 0.23\n",
      "Epoch: 90 Train Loss: 0.2590 Acc: 0.8914 Pre: 0.8717 Recall: 0.9178 F1: 0.8942 Train AUC: 0.9599 Val AUC: 0.9619 Val PRC: 0.9577 Time: 0.24\n",
      "Epoch: 91 Train Loss: 0.2526 Acc: 0.8943 Pre: 0.9189 Recall: 0.8650 F1: 0.8911 Train AUC: 0.9609 Val AUC: 0.9577 Val PRC: 0.9556 Time: 0.23\n",
      "Epoch: 92 Train Loss: 0.2418 Acc: 0.8904 Pre: 0.8743 Recall: 0.9119 F1: 0.8927 Train AUC: 0.9650 Val AUC: 0.9623 Val PRC: 0.9573 Time: 0.23\n",
      "Epoch: 93 Train Loss: 0.2484 Acc: 0.8777 Pre: 0.8614 Recall: 0.9002 F1: 0.8804 Train AUC: 0.9621 Val AUC: 0.9580 Val PRC: 0.9601 Time: 0.24\n",
      "Epoch: 94 Train Loss: 0.2433 Acc: 0.8816 Pre: 0.8507 Recall: 0.9256 F1: 0.8866 Train AUC: 0.9640 Val AUC: 0.9577 Val PRC: 0.9573 Time: 0.23\n",
      "Epoch: 95 Train Loss: 0.2420 Acc: 0.8865 Pre: 0.8664 Recall: 0.9139 F1: 0.8895 Train AUC: 0.9639 Val AUC: 0.9634 Val PRC: 0.9636 Time: 0.24\n",
      "Epoch: 96 Train Loss: 0.2457 Acc: 0.9002 Pre: 0.8910 Recall: 0.9119 F1: 0.9014 Train AUC: 0.9625 Val AUC: 0.9630 Val PRC: 0.9582 Time: 0.23\n",
      "Epoch: 97 Train Loss: 0.2472 Acc: 0.8933 Pre: 0.9020 Recall: 0.8826 F1: 0.8922 Train AUC: 0.9623 Val AUC: 0.9609 Val PRC: 0.9566 Time: 0.24\n",
      "Epoch: 98 Train Loss: 0.2512 Acc: 0.8904 Pre: 0.8701 Recall: 0.9178 F1: 0.8933 Train AUC: 0.9629 Val AUC: 0.9623 Val PRC: 0.9613 Time: 0.24\n",
      "Epoch: 99 Train Loss: 0.2356 Acc: 0.8963 Pre: 0.8757 Recall: 0.9237 F1: 0.8990 Train AUC: 0.9661 Val AUC: 0.9599 Val PRC: 0.9554 Time: 0.24\n",
      "Epoch: 100 Train Loss: 0.2452 Acc: 0.8767 Pre: 0.8431 Recall: 0.9256 F1: 0.8825 Train AUC: 0.9645 Val AUC: 0.9567 Val PRC: 0.9577 Time: 0.23\n",
      "Epoch: 101 Train Loss: 0.2405 Acc: 0.8845 Pre: 0.8453 Recall: 0.9413 F1: 0.8907 Train AUC: 0.9644 Val AUC: 0.9603 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 102 Train Loss: 0.2264 Acc: 0.8914 Pre: 0.8745 Recall: 0.9139 F1: 0.8938 Train AUC: 0.9694 Val AUC: 0.9646 Val PRC: 0.9650 Time: 0.24\n",
      "Epoch: 103 Train Loss: 0.2375 Acc: 0.8924 Pre: 0.8762 Recall: 0.9139 F1: 0.8946 Train AUC: 0.9656 Val AUC: 0.9630 Val PRC: 0.9619 Time: 0.23\n",
      "Epoch: 104 Train Loss: 0.2408 Acc: 0.9002 Pre: 0.8910 Recall: 0.9119 F1: 0.9014 Train AUC: 0.9654 Val AUC: 0.9665 Val PRC: 0.9654 Time: 0.24\n",
      "Epoch: 105 Train Loss: 0.2371 Acc: 0.8963 Pre: 0.8757 Recall: 0.9237 F1: 0.8990 Train AUC: 0.9654 Val AUC: 0.9645 Val PRC: 0.9597 Time: 0.24\n",
      "Epoch: 106 Train Loss: 0.2285 Acc: 0.8963 Pre: 0.8571 Recall: 0.9511 F1: 0.9017 Train AUC: 0.9684 Val AUC: 0.9637 Val PRC: 0.9613 Time: 0.24\n",
      "Epoch: 107 Train Loss: 0.2328 Acc: 0.8933 Pre: 0.8708 Recall: 0.9237 F1: 0.8965 Train AUC: 0.9675 Val AUC: 0.9629 Val PRC: 0.9599 Time: 0.24\n",
      "Epoch: 108 Train Loss: 0.2403 Acc: 0.9022 Pre: 0.9006 Recall: 0.9041 F1: 0.9023 Train AUC: 0.9655 Val AUC: 0.9651 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 109 Train Loss: 0.2301 Acc: 0.9022 Pre: 0.9069 Recall: 0.8963 F1: 0.9016 Train AUC: 0.9688 Val AUC: 0.9609 Val PRC: 0.9585 Time: 0.23\n",
      "Epoch: 110 Train Loss: 0.2328 Acc: 0.8963 Pre: 0.8947 Recall: 0.8982 F1: 0.8965 Train AUC: 0.9664 Val AUC: 0.9610 Val PRC: 0.9600 Time: 0.23\n",
      "Epoch: 111 Train Loss: 0.2325 Acc: 0.8904 Pre: 0.8815 Recall: 0.9022 F1: 0.8917 Train AUC: 0.9668 Val AUC: 0.9597 Val PRC: 0.9593 Time: 0.23\n",
      "Epoch: 112 Train Loss: 0.2278 Acc: 0.8953 Pre: 0.8855 Recall: 0.9080 F1: 0.8966 Train AUC: 0.9686 Val AUC: 0.9628 Val PRC: 0.9590 Time: 0.23\n",
      "Epoch: 113 Train Loss: 0.2249 Acc: 0.8914 Pre: 0.8891 Recall: 0.8943 F1: 0.8917 Train AUC: 0.9697 Val AUC: 0.9608 Val PRC: 0.9617 Time: 0.23\n",
      "Epoch: 114 Train Loss: 0.2453 Acc: 0.9012 Pre: 0.9201 Recall: 0.8787 F1: 0.8989 Train AUC: 0.9627 Val AUC: 0.9618 Val PRC: 0.9565 Time: 0.23\n",
      "Epoch: 115 Train Loss: 0.2300 Acc: 0.8924 Pre: 0.8734 Recall: 0.9178 F1: 0.8950 Train AUC: 0.9678 Val AUC: 0.9627 Val PRC: 0.9610 Time: 0.23\n",
      "Epoch: 116 Train Loss: 0.2261 Acc: 0.9012 Pre: 0.8912 Recall: 0.9139 F1: 0.9024 Train AUC: 0.9690 Val AUC: 0.9640 Val PRC: 0.9589 Time: 0.24\n",
      "Epoch: 117 Train Loss: 0.2399 Acc: 0.8894 Pre: 0.8467 Recall: 0.9511 F1: 0.8959 Train AUC: 0.9648 Val AUC: 0.9618 Val PRC: 0.9577 Time: 0.24\n",
      "Epoch: 118 Train Loss: 0.2386 Acc: 0.9012 Pre: 0.8714 Recall: 0.9413 F1: 0.9050 Train AUC: 0.9653 Val AUC: 0.9666 Val PRC: 0.9627 Time: 0.23\n",
      "Epoch: 119 Train Loss: 0.2288 Acc: 0.8963 Pre: 0.8917 Recall: 0.9022 F1: 0.8969 Train AUC: 0.9683 Val AUC: 0.9634 Val PRC: 0.9589 Time: 0.24\n",
      "Epoch: 120 Train Loss: 0.2239 Acc: 0.9051 Pre: 0.8981 Recall: 0.9139 F1: 0.9059 Train AUC: 0.9696 Val AUC: 0.9651 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 121 Train Loss: 0.2257 Acc: 0.8992 Pre: 0.8750 Recall: 0.9315 F1: 0.9024 Train AUC: 0.9696 Val AUC: 0.9640 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 122 Train Loss: 0.2265 Acc: 0.8914 Pre: 0.8597 Recall: 0.9354 F1: 0.8960 Train AUC: 0.9687 Val AUC: 0.9641 Val PRC: 0.9623 Time: 0.23\n",
      "Epoch: 123 Train Loss: 0.2196 Acc: 0.8982 Pre: 0.8720 Recall: 0.9335 F1: 0.9017 Train AUC: 0.9710 Val AUC: 0.9651 Val PRC: 0.9614 Time: 0.24\n",
      "Epoch: 124 Train Loss: 0.2203 Acc: 0.8963 Pre: 0.8828 Recall: 0.9139 F1: 0.8981 Train AUC: 0.9708 Val AUC: 0.9646 Val PRC: 0.9641 Time: 0.24\n",
      "Epoch: 125 Train Loss: 0.2239 Acc: 0.8982 Pre: 0.8967 Recall: 0.9002 F1: 0.8984 Train AUC: 0.9700 Val AUC: 0.9630 Val PRC: 0.9591 Time: 0.24\n",
      "Epoch: 126 Train Loss: 0.2212 Acc: 0.9022 Pre: 0.8841 Recall: 0.9256 F1: 0.9044 Train AUC: 0.9703 Val AUC: 0.9651 Val PRC: 0.9649 Time: 0.25\n",
      "Epoch: 127 Train Loss: 0.2127 Acc: 0.9031 Pre: 0.8916 Recall: 0.9178 F1: 0.9045 Train AUC: 0.9725 Val AUC: 0.9631 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 128 Train Loss: 0.2316 Acc: 0.9002 Pre: 0.8766 Recall: 0.9315 F1: 0.9032 Train AUC: 0.9674 Val AUC: 0.9659 Val PRC: 0.9612 Time: 0.23\n",
      "Epoch: 129 Train Loss: 0.2165 Acc: 0.9070 Pre: 0.8985 Recall: 0.9178 F1: 0.9080 Train AUC: 0.9709 Val AUC: 0.9645 Val PRC: 0.9552 Time: 0.23\n",
      "Epoch: 130 Train Loss: 0.2222 Acc: 0.9012 Pre: 0.8755 Recall: 0.9354 F1: 0.9044 Train AUC: 0.9695 Val AUC: 0.9650 Val PRC: 0.9568 Time: 0.23\n",
      "Epoch: 131 Train Loss: 0.2213 Acc: 0.9051 Pre: 0.8891 Recall: 0.9256 F1: 0.9070 Train AUC: 0.9704 Val AUC: 0.9669 Val PRC: 0.9649 Time: 0.23\n",
      "Epoch: 132 Train Loss: 0.2095 Acc: 0.9080 Pre: 0.8826 Recall: 0.9413 F1: 0.9110 Train AUC: 0.9733 Val AUC: 0.9682 Val PRC: 0.9625 Time: 0.23\n",
      "Epoch: 133 Train Loss: 0.2103 Acc: 0.9012 Pre: 0.8810 Recall: 0.9276 F1: 0.9037 Train AUC: 0.9731 Val AUC: 0.9662 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 134 Train Loss: 0.2278 Acc: 0.9080 Pre: 0.8912 Recall: 0.9295 F1: 0.9100 Train AUC: 0.9692 Val AUC: 0.9693 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 135 Train Loss: 0.2206 Acc: 0.9041 Pre: 0.9089 Recall: 0.8982 F1: 0.9035 Train AUC: 0.9703 Val AUC: 0.9655 Val PRC: 0.9659 Time: 0.24\n",
      "Epoch: 136 Train Loss: 0.2174 Acc: 0.8992 Pre: 0.8820 Recall: 0.9217 F1: 0.9014 Train AUC: 0.9712 Val AUC: 0.9653 Val PRC: 0.9627 Time: 0.23\n",
      "Epoch: 137 Train Loss: 0.2130 Acc: 0.8943 Pre: 0.8752 Recall: 0.9198 F1: 0.8969 Train AUC: 0.9734 Val AUC: 0.9647 Val PRC: 0.9649 Time: 0.24\n",
      "Epoch: 138 Train Loss: 0.2134 Acc: 0.9031 Pre: 0.8787 Recall: 0.9354 F1: 0.9062 Train AUC: 0.9720 Val AUC: 0.9636 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 139 Train Loss: 0.2007 Acc: 0.9051 Pre: 0.8891 Recall: 0.9256 F1: 0.9070 Train AUC: 0.9757 Val AUC: 0.9668 Val PRC: 0.9620 Time: 0.23\n",
      "Epoch: 140 Train Loss: 0.2050 Acc: 0.9041 Pre: 0.9041 Recall: 0.9041 F1: 0.9041 Train AUC: 0.9744 Val AUC: 0.9645 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 141 Train Loss: 0.2154 Acc: 0.9080 Pre: 0.8971 Recall: 0.9217 F1: 0.9093 Train AUC: 0.9715 Val AUC: 0.9668 Val PRC: 0.9610 Time: 0.24\n",
      "Epoch: 142 Train Loss: 0.1992 Acc: 0.9100 Pre: 0.9267 Recall: 0.8904 F1: 0.9082 Train AUC: 0.9766 Val AUC: 0.9713 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 143 Train Loss: 0.2139 Acc: 0.9070 Pre: 0.8824 Recall: 0.9393 F1: 0.9100 Train AUC: 0.9721 Val AUC: 0.9694 Val PRC: 0.9687 Time: 0.24\n",
      "Epoch: 144 Train Loss: 0.2114 Acc: 0.9041 Pre: 0.8734 Recall: 0.9452 F1: 0.9079 Train AUC: 0.9727 Val AUC: 0.9634 Val PRC: 0.9619 Time: 0.24\n",
      "Epoch: 145 Train Loss: 0.2128 Acc: 0.9022 Pre: 0.8757 Recall: 0.9374 F1: 0.9055 Train AUC: 0.9724 Val AUC: 0.9652 Val PRC: 0.9640 Time: 0.24\n",
      "Epoch: 146 Train Loss: 0.2149 Acc: 0.9051 Pre: 0.8819 Recall: 0.9354 F1: 0.9079 Train AUC: 0.9718 Val AUC: 0.9656 Val PRC: 0.9656 Time: 0.24\n",
      "Epoch: 147 Train Loss: 0.2129 Acc: 0.9051 Pre: 0.8920 Recall: 0.9217 F1: 0.9066 Train AUC: 0.9716 Val AUC: 0.9667 Val PRC: 0.9653 Time: 0.23\n",
      "Epoch: 148 Train Loss: 0.1992 Acc: 0.9031 Pre: 0.9153 Recall: 0.8885 F1: 0.9017 Train AUC: 0.9766 Val AUC: 0.9682 Val PRC: 0.9671 Time: 0.23\n",
      "Epoch: 149 Train Loss: 0.2035 Acc: 0.9022 Pre: 0.8899 Recall: 0.9178 F1: 0.9037 Train AUC: 0.9745 Val AUC: 0.9669 Val PRC: 0.9608 Time: 0.23\n",
      "Epoch: 150 Train Loss: 0.2159 Acc: 0.8982 Pre: 0.8707 Recall: 0.9354 F1: 0.9019 Train AUC: 0.9715 Val AUC: 0.9651 Val PRC: 0.9635 Time: 0.23\n",
      "Epoch: 151 Train Loss: 0.2020 Acc: 0.9031 Pre: 0.8887 Recall: 0.9217 F1: 0.9049 Train AUC: 0.9749 Val AUC: 0.9673 Val PRC: 0.9654 Time: 0.23\n",
      "Epoch: 152 Train Loss: 0.2171 Acc: 0.9061 Pre: 0.9029 Recall: 0.9100 F1: 0.9064 Train AUC: 0.9706 Val AUC: 0.9684 Val PRC: 0.9684 Time: 0.24\n",
      "Epoch: 153 Train Loss: 0.2173 Acc: 0.9061 Pre: 0.8952 Recall: 0.9198 F1: 0.9073 Train AUC: 0.9710 Val AUC: 0.9677 Val PRC: 0.9673 Time: 0.24\n",
      "Epoch: 154 Train Loss: 0.2112 Acc: 0.9061 Pre: 0.8922 Recall: 0.9237 F1: 0.9077 Train AUC: 0.9734 Val AUC: 0.9675 Val PRC: 0.9607 Time: 0.25\n",
      "Epoch: 155 Train Loss: 0.1999 Acc: 0.9051 Pre: 0.8736 Recall: 0.9472 F1: 0.9089 Train AUC: 0.9761 Val AUC: 0.9666 Val PRC: 0.9625 Time: 0.23\n",
      "Epoch: 156 Train Loss: 0.2026 Acc: 0.9061 Pre: 0.8908 Recall: 0.9256 F1: 0.9079 Train AUC: 0.9755 Val AUC: 0.9673 Val PRC: 0.9648 Time: 0.23\n",
      "Epoch: 157 Train Loss: 0.2124 Acc: 0.9051 Pre: 0.8777 Recall: 0.9413 F1: 0.9084 Train AUC: 0.9725 Val AUC: 0.9640 Val PRC: 0.9612 Time: 0.23\n",
      "Epoch: 158 Train Loss: 0.1936 Acc: 0.9100 Pre: 0.8830 Recall: 0.9452 F1: 0.9130 Train AUC: 0.9774 Val AUC: 0.9703 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 159 Train Loss: 0.2082 Acc: 0.9002 Pre: 0.8940 Recall: 0.9080 F1: 0.9010 Train AUC: 0.9739 Val AUC: 0.9680 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 160 Train Loss: 0.1993 Acc: 0.9051 Pre: 0.8906 Recall: 0.9237 F1: 0.9068 Train AUC: 0.9762 Val AUC: 0.9706 Val PRC: 0.9705 Time: 0.23\n",
      "Epoch: 161 Train Loss: 0.1998 Acc: 0.9031 Pre: 0.8692 Recall: 0.9491 F1: 0.9074 Train AUC: 0.9756 Val AUC: 0.9664 Val PRC: 0.9657 Time: 0.23\n",
      "Epoch: 162 Train Loss: 0.1961 Acc: 0.9110 Pre: 0.8875 Recall: 0.9413 F1: 0.9136 Train AUC: 0.9767 Val AUC: 0.9680 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 163 Train Loss: 0.1878 Acc: 0.9080 Pre: 0.8912 Recall: 0.9295 F1: 0.9100 Train AUC: 0.9782 Val AUC: 0.9689 Val PRC: 0.9670 Time: 0.24\n",
      "Epoch: 164 Train Loss: 0.1999 Acc: 0.9139 Pre: 0.8983 Recall: 0.9335 F1: 0.9155 Train AUC: 0.9751 Val AUC: 0.9689 Val PRC: 0.9662 Time: 0.23\n",
      "Epoch: 165 Train Loss: 0.2267 Acc: 0.9100 Pre: 0.9267 Recall: 0.8904 F1: 0.9082 Train AUC: 0.9744 Val AUC: 0.9698 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 166 Train Loss: 0.1976 Acc: 0.9139 Pre: 0.9091 Recall: 0.9198 F1: 0.9144 Train AUC: 0.9757 Val AUC: 0.9702 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 167 Train Loss: 0.1932 Acc: 0.9139 Pre: 0.9029 Recall: 0.9276 F1: 0.9151 Train AUC: 0.9772 Val AUC: 0.9687 Val PRC: 0.9683 Time: 0.23\n",
      "Epoch: 168 Train Loss: 0.1917 Acc: 0.9041 Pre: 0.8817 Recall: 0.9335 F1: 0.9068 Train AUC: 0.9775 Val AUC: 0.9713 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 169 Train Loss: 0.1949 Acc: 0.9100 Pre: 0.8858 Recall: 0.9413 F1: 0.9127 Train AUC: 0.9766 Val AUC: 0.9715 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 170 Train Loss: 0.1923 Acc: 0.9119 Pre: 0.8964 Recall: 0.9315 F1: 0.9136 Train AUC: 0.9774 Val AUC: 0.9676 Val PRC: 0.9676 Time: 0.24\n",
      "Epoch: 171 Train Loss: 0.1909 Acc: 0.9139 Pre: 0.8983 Recall: 0.9335 F1: 0.9155 Train AUC: 0.9775 Val AUC: 0.9657 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 172 Train Loss: 0.1904 Acc: 0.9119 Pre: 0.8920 Recall: 0.9374 F1: 0.9141 Train AUC: 0.9779 Val AUC: 0.9687 Val PRC: 0.9685 Time: 0.23\n",
      "Epoch: 173 Train Loss: 0.1909 Acc: 0.9119 Pre: 0.8862 Recall: 0.9452 F1: 0.9148 Train AUC: 0.9774 Val AUC: 0.9696 Val PRC: 0.9688 Time: 0.23\n",
      "Epoch: 174 Train Loss: 0.1859 Acc: 0.9168 Pre: 0.8989 Recall: 0.9393 F1: 0.9187 Train AUC: 0.9790 Val AUC: 0.9666 Val PRC: 0.9648 Time: 0.23\n",
      "Epoch: 175 Train Loss: 0.1782 Acc: 0.9119 Pre: 0.8820 Recall: 0.9511 F1: 0.9153 Train AUC: 0.9805 Val AUC: 0.9687 Val PRC: 0.9632 Time: 0.23\n",
      "Epoch: 176 Train Loss: 0.1815 Acc: 0.9070 Pre: 0.8824 Recall: 0.9393 F1: 0.9100 Train AUC: 0.9797 Val AUC: 0.9663 Val PRC: 0.9660 Time: 0.23\n",
      "Epoch: 177 Train Loss: 0.1913 Acc: 0.9100 Pre: 0.8830 Recall: 0.9452 F1: 0.9130 Train AUC: 0.9772 Val AUC: 0.9703 Val PRC: 0.9686 Time: 0.23\n",
      "Epoch: 178 Train Loss: 0.1918 Acc: 0.9129 Pre: 0.8981 Recall: 0.9315 F1: 0.9145 Train AUC: 0.9775 Val AUC: 0.9700 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 179 Train Loss: 0.1967 Acc: 0.9080 Pre: 0.8717 Recall: 0.9569 F1: 0.9123 Train AUC: 0.9760 Val AUC: 0.9682 Val PRC: 0.9672 Time: 0.23\n",
      "Epoch: 180 Train Loss: 0.1930 Acc: 0.9110 Pre: 0.9234 Recall: 0.8963 F1: 0.9096 Train AUC: 0.9764 Val AUC: 0.9706 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 181 Train Loss: 0.1822 Acc: 0.9119 Pre: 0.9025 Recall: 0.9237 F1: 0.9130 Train AUC: 0.9791 Val AUC: 0.9685 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 182 Train Loss: 0.1955 Acc: 0.9041 Pre: 0.8642 Recall: 0.9589 F1: 0.9091 Train AUC: 0.9756 Val AUC: 0.9651 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 183 Train Loss: 0.1819 Acc: 0.9100 Pre: 0.8960 Recall: 0.9276 F1: 0.9115 Train AUC: 0.9801 Val AUC: 0.9675 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 184 Train Loss: 0.1773 Acc: 0.9100 Pre: 0.9068 Recall: 0.9139 F1: 0.9103 Train AUC: 0.9808 Val AUC: 0.9714 Val PRC: 0.9684 Time: 0.23\n",
      "Epoch: 185 Train Loss: 0.1739 Acc: 0.9119 Pre: 0.8905 Recall: 0.9393 F1: 0.9143 Train AUC: 0.9816 Val AUC: 0.9691 Val PRC: 0.9632 Time: 0.23\n",
      "Epoch: 186 Train Loss: 0.1835 Acc: 0.9022 Pre: 0.8730 Recall: 0.9413 F1: 0.9058 Train AUC: 0.9785 Val AUC: 0.9678 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 187 Train Loss: 0.1813 Acc: 0.9139 Pre: 0.9091 Recall: 0.9198 F1: 0.9144 Train AUC: 0.9791 Val AUC: 0.9705 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 188 Train Loss: 0.1793 Acc: 0.9149 Pre: 0.9125 Recall: 0.9178 F1: 0.9151 Train AUC: 0.9801 Val AUC: 0.9704 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 189 Train Loss: 0.1767 Acc: 0.9159 Pre: 0.9048 Recall: 0.9295 F1: 0.9170 Train AUC: 0.9800 Val AUC: 0.9697 Val PRC: 0.9690 Time: 0.39\n",
      "Epoch: 190 Train Loss: 0.1770 Acc: 0.9100 Pre: 0.8734 Recall: 0.9589 F1: 0.9142 Train AUC: 0.9797 Val AUC: 0.9691 Val PRC: 0.9690 Time: 0.23\n",
      "Epoch: 191 Train Loss: 0.1828 Acc: 0.9207 Pre: 0.8952 Recall: 0.9530 F1: 0.9232 Train AUC: 0.9786 Val AUC: 0.9694 Val PRC: 0.9663 Time: 0.24\n",
      "Epoch: 192 Train Loss: 0.1831 Acc: 0.9100 Pre: 0.8960 Recall: 0.9276 F1: 0.9115 Train AUC: 0.9789 Val AUC: 0.9691 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 193 Train Loss: 0.1851 Acc: 0.9149 Pre: 0.9206 Recall: 0.9080 F1: 0.9143 Train AUC: 0.9788 Val AUC: 0.9685 Val PRC: 0.9693 Time: 0.23\n",
      "Epoch: 194 Train Loss: 0.1774 Acc: 0.9129 Pre: 0.9027 Recall: 0.9256 F1: 0.9140 Train AUC: 0.9801 Val AUC: 0.9692 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 195 Train Loss: 0.1923 Acc: 0.9022 Pre: 0.8689 Recall: 0.9472 F1: 0.9064 Train AUC: 0.9765 Val AUC: 0.9634 Val PRC: 0.9564 Time: 0.23\n",
      "Epoch: 196 Train Loss: 0.1857 Acc: 0.9168 Pre: 0.9004 Recall: 0.9374 F1: 0.9185 Train AUC: 0.9790 Val AUC: 0.9676 Val PRC: 0.9670 Time: 0.23\n",
      "Epoch: 197 Train Loss: 0.1664 Acc: 0.9178 Pre: 0.9178 Recall: 0.9178 F1: 0.9178 Train AUC: 0.9830 Val AUC: 0.9703 Val PRC: 0.9708 Time: 0.23\n",
      "Epoch: 198 Train Loss: 0.1768 Acc: 0.9139 Pre: 0.8895 Recall: 0.9452 F1: 0.9165 Train AUC: 0.9805 Val AUC: 0.9691 Val PRC: 0.9693 Time: 0.23\n",
      "Epoch: 199 Train Loss: 0.1731 Acc: 0.9129 Pre: 0.8966 Recall: 0.9335 F1: 0.9147 Train AUC: 0.9811 Val AUC: 0.9684 Val PRC: 0.9672 Time: 0.23\n",
      "Epoch: 200 Train Loss: 0.1928 Acc: 0.9159 Pre: 0.8871 Recall: 0.9530 F1: 0.9189 Train AUC: 0.9771 Val AUC: 0.9692 Val PRC: 0.9680 Time: 0.23\n",
      "Epoch: 201 Train Loss: 0.1711 Acc: 0.9149 Pre: 0.8985 Recall: 0.9354 F1: 0.9166 Train AUC: 0.9815 Val AUC: 0.9695 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 202 Train Loss: 0.1762 Acc: 0.9149 Pre: 0.9061 Recall: 0.9256 F1: 0.9158 Train AUC: 0.9814 Val AUC: 0.9706 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 203 Train Loss: 0.1811 Acc: 0.9149 Pre: 0.9015 Recall: 0.9315 F1: 0.9163 Train AUC: 0.9792 Val AUC: 0.9690 Val PRC: 0.9680 Time: 0.23\n",
      "Epoch: 204 Train Loss: 0.1686 Acc: 0.9159 Pre: 0.8928 Recall: 0.9452 F1: 0.9183 Train AUC: 0.9829 Val AUC: 0.9673 Val PRC: 0.9657 Time: 0.23\n",
      "Epoch: 205 Train Loss: 0.1792 Acc: 0.9110 Pre: 0.9150 Recall: 0.9061 F1: 0.9105 Train AUC: 0.9793 Val AUC: 0.9669 Val PRC: 0.9651 Time: 0.23\n",
      "Epoch: 206 Train Loss: 0.1825 Acc: 0.9139 Pre: 0.8797 Recall: 0.9589 F1: 0.9176 Train AUC: 0.9803 Val AUC: 0.9708 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 207 Train Loss: 0.1645 Acc: 0.9159 Pre: 0.8987 Recall: 0.9374 F1: 0.9176 Train AUC: 0.9834 Val AUC: 0.9709 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 208 Train Loss: 0.1735 Acc: 0.9070 Pre: 0.8675 Recall: 0.9609 F1: 0.9118 Train AUC: 0.9813 Val AUC: 0.9709 Val PRC: 0.9710 Time: 0.23\n",
      "Epoch: 209 Train Loss: 0.1836 Acc: 0.9237 Pre: 0.9124 Recall: 0.9374 F1: 0.9247 Train AUC: 0.9792 Val AUC: 0.9690 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 210 Train Loss: 0.1775 Acc: 0.9149 Pre: 0.9000 Recall: 0.9335 F1: 0.9164 Train AUC: 0.9803 Val AUC: 0.9735 Val PRC: 0.9736 Time: 0.25\n",
      "Epoch: 211 Train Loss: 0.1825 Acc: 0.9198 Pre: 0.8994 Recall: 0.9452 F1: 0.9218 Train AUC: 0.9813 Val AUC: 0.9744 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 212 Train Loss: 0.1694 Acc: 0.9207 Pre: 0.9232 Recall: 0.9178 F1: 0.9205 Train AUC: 0.9813 Val AUC: 0.9711 Val PRC: 0.9712 Time: 0.24\n",
      "Epoch: 213 Train Loss: 0.1842 Acc: 0.9139 Pre: 0.8867 Recall: 0.9491 F1: 0.9168 Train AUC: 0.9798 Val AUC: 0.9675 Val PRC: 0.9669 Time: 0.23\n",
      "Epoch: 214 Train Loss: 0.1844 Acc: 0.9188 Pre: 0.9163 Recall: 0.9217 F1: 0.9190 Train AUC: 0.9783 Val AUC: 0.9673 Val PRC: 0.9650 Time: 0.23\n",
      "Epoch: 215 Train Loss: 0.1611 Acc: 0.9159 Pre: 0.9208 Recall: 0.9100 F1: 0.9154 Train AUC: 0.9838 Val AUC: 0.9712 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 216 Train Loss: 0.1757 Acc: 0.9159 Pre: 0.8913 Recall: 0.9472 F1: 0.9184 Train AUC: 0.9818 Val AUC: 0.9696 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 217 Train Loss: 0.1675 Acc: 0.9129 Pre: 0.8879 Recall: 0.9452 F1: 0.9156 Train AUC: 0.9819 Val AUC: 0.9692 Val PRC: 0.9686 Time: 0.23\n",
      "Epoch: 218 Train Loss: 0.1816 Acc: 0.9149 Pre: 0.8813 Recall: 0.9589 F1: 0.9185 Train AUC: 0.9800 Val AUC: 0.9706 Val PRC: 0.9710 Time: 0.23\n",
      "Epoch: 219 Train Loss: 0.1598 Acc: 0.9198 Pre: 0.9024 Recall: 0.9413 F1: 0.9215 Train AUC: 0.9833 Val AUC: 0.9693 Val PRC: 0.9665 Time: 0.24\n",
      "Epoch: 220 Train Loss: 0.1659 Acc: 0.9207 Pre: 0.9317 Recall: 0.9080 F1: 0.9197 Train AUC: 0.9825 Val AUC: 0.9734 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 221 Train Loss: 0.1661 Acc: 0.9178 Pre: 0.9261 Recall: 0.9080 F1: 0.9170 Train AUC: 0.9821 Val AUC: 0.9744 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 222 Train Loss: 0.1659 Acc: 0.9149 Pre: 0.9061 Recall: 0.9256 F1: 0.9158 Train AUC: 0.9818 Val AUC: 0.9670 Val PRC: 0.9650 Time: 0.23\n",
      "Epoch: 223 Train Loss: 0.1837 Acc: 0.9168 Pre: 0.8873 Recall: 0.9550 F1: 0.9199 Train AUC: 0.9763 Val AUC: 0.9643 Val PRC: 0.9566 Time: 0.23\n",
      "Epoch: 224 Train Loss: 0.1656 Acc: 0.9110 Pre: 0.9070 Recall: 0.9159 F1: 0.9114 Train AUC: 0.9823 Val AUC: 0.9702 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 225 Train Loss: 0.1691 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9809 Val AUC: 0.9705 Val PRC: 0.9705 Time: 0.23\n",
      "Epoch: 226 Train Loss: 0.1581 Acc: 0.9188 Pre: 0.9007 Recall: 0.9413 F1: 0.9206 Train AUC: 0.9842 Val AUC: 0.9711 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 227 Train Loss: 0.1638 Acc: 0.9188 Pre: 0.9007 Recall: 0.9413 F1: 0.9206 Train AUC: 0.9820 Val AUC: 0.9711 Val PRC: 0.9704 Time: 0.24\n",
      "Epoch: 228 Train Loss: 0.1439 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9864 Val AUC: 0.9728 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 229 Train Loss: 0.1749 Acc: 0.9178 Pre: 0.9162 Recall: 0.9198 F1: 0.9180 Train AUC: 0.9801 Val AUC: 0.9685 Val PRC: 0.9648 Time: 0.24\n",
      "Epoch: 230 Train Loss: 0.1643 Acc: 0.9110 Pre: 0.8791 Recall: 0.9530 F1: 0.9146 Train AUC: 0.9827 Val AUC: 0.9705 Val PRC: 0.9679 Time: 0.24\n",
      "Epoch: 231 Train Loss: 0.1771 Acc: 0.9168 Pre: 0.9065 Recall: 0.9295 F1: 0.9179 Train AUC: 0.9816 Val AUC: 0.9713 Val PRC: 0.9717 Time: 0.24\n",
      "Epoch: 232 Train Loss: 0.1802 Acc: 0.9149 Pre: 0.8911 Recall: 0.9452 F1: 0.9174 Train AUC: 0.9841 Val AUC: 0.9711 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 233 Train Loss: 0.1785 Acc: 0.9178 Pre: 0.9006 Recall: 0.9393 F1: 0.9195 Train AUC: 0.9797 Val AUC: 0.9722 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 234 Train Loss: 0.1628 Acc: 0.9159 Pre: 0.9225 Recall: 0.9080 F1: 0.9152 Train AUC: 0.9834 Val AUC: 0.9719 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 235 Train Loss: 0.1723 Acc: 0.9119 Pre: 0.8752 Recall: 0.9609 F1: 0.9160 Train AUC: 0.9809 Val AUC: 0.9720 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 236 Train Loss: 0.1591 Acc: 0.9207 Pre: 0.9151 Recall: 0.9276 F1: 0.9213 Train AUC: 0.9844 Val AUC: 0.9741 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 237 Train Loss: 0.1541 Acc: 0.9159 Pre: 0.9017 Recall: 0.9335 F1: 0.9173 Train AUC: 0.9848 Val AUC: 0.9728 Val PRC: 0.9724 Time: 0.23\n",
      "Epoch: 238 Train Loss: 0.1664 Acc: 0.9012 Pre: 0.8596 Recall: 0.9589 F1: 0.9066 Train AUC: 0.9826 Val AUC: 0.9682 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 239 Train Loss: 0.1667 Acc: 0.9149 Pre: 0.9109 Recall: 0.9198 F1: 0.9153 Train AUC: 0.9812 Val AUC: 0.9711 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 240 Train Loss: 0.1455 Acc: 0.9100 Pre: 0.8748 Recall: 0.9569 F1: 0.9140 Train AUC: 0.9869 Val AUC: 0.9713 Val PRC: 0.9713 Time: 0.24\n",
      "Epoch: 241 Train Loss: 0.1659 Acc: 0.9198 Pre: 0.9133 Recall: 0.9276 F1: 0.9204 Train AUC: 0.9826 Val AUC: 0.9728 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 242 Train Loss: 0.1523 Acc: 0.9159 Pre: 0.8722 Recall: 0.9746 F1: 0.9205 Train AUC: 0.9839 Val AUC: 0.9736 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 243 Train Loss: 0.1627 Acc: 0.9178 Pre: 0.9194 Recall: 0.9159 F1: 0.9176 Train AUC: 0.9835 Val AUC: 0.9701 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 244 Train Loss: 0.1590 Acc: 0.9217 Pre: 0.8998 Recall: 0.9491 F1: 0.9238 Train AUC: 0.9829 Val AUC: 0.9720 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 245 Train Loss: 0.1577 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9842 Val AUC: 0.9727 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 246 Train Loss: 0.1496 Acc: 0.9237 Pre: 0.9032 Recall: 0.9491 F1: 0.9256 Train AUC: 0.9857 Val AUC: 0.9745 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 247 Train Loss: 0.1489 Acc: 0.9256 Pre: 0.9020 Recall: 0.9550 F1: 0.9278 Train AUC: 0.9863 Val AUC: 0.9737 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 248 Train Loss: 0.1592 Acc: 0.9178 Pre: 0.9082 Recall: 0.9295 F1: 0.9188 Train AUC: 0.9829 Val AUC: 0.9694 Val PRC: 0.9684 Time: 0.24\n",
      "Epoch: 249 Train Loss: 0.1498 Acc: 0.9315 Pre: 0.9168 Recall: 0.9491 F1: 0.9327 Train AUC: 0.9848 Val AUC: 0.9762 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 250 Train Loss: 0.1527 Acc: 0.9286 Pre: 0.9163 Recall: 0.9432 F1: 0.9296 Train AUC: 0.9853 Val AUC: 0.9763 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 251 Train Loss: 0.1494 Acc: 0.9227 Pre: 0.9202 Recall: 0.9256 F1: 0.9229 Train AUC: 0.9855 Val AUC: 0.9737 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 252 Train Loss: 0.1638 Acc: 0.9207 Pre: 0.8996 Recall: 0.9472 F1: 0.9228 Train AUC: 0.9820 Val AUC: 0.9702 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 253 Train Loss: 0.1489 Acc: 0.9188 Pre: 0.9068 Recall: 0.9335 F1: 0.9200 Train AUC: 0.9849 Val AUC: 0.9708 Val PRC: 0.9688 Time: 0.23\n",
      "Epoch: 254 Train Loss: 0.1448 Acc: 0.9198 Pre: 0.8921 Recall: 0.9550 F1: 0.9225 Train AUC: 0.9853 Val AUC: 0.9677 Val PRC: 0.9625 Time: 0.24\n",
      "Epoch: 255 Train Loss: 0.1412 Acc: 0.9178 Pre: 0.8917 Recall: 0.9511 F1: 0.9205 Train AUC: 0.9862 Val AUC: 0.9718 Val PRC: 0.9703 Time: 0.24\n",
      "Epoch: 256 Train Loss: 0.1441 Acc: 0.9305 Pre: 0.9198 Recall: 0.9432 F1: 0.9314 Train AUC: 0.9862 Val AUC: 0.9733 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 257 Train Loss: 0.1579 Acc: 0.9198 Pre: 0.9024 Recall: 0.9413 F1: 0.9215 Train AUC: 0.9823 Val AUC: 0.9706 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 258 Train Loss: 0.1613 Acc: 0.9207 Pre: 0.9167 Recall: 0.9256 F1: 0.9211 Train AUC: 0.9815 Val AUC: 0.9693 Val PRC: 0.9664 Time: 0.23\n",
      "Epoch: 259 Train Loss: 0.1408 Acc: 0.9207 Pre: 0.9370 Recall: 0.9022 F1: 0.9192 Train AUC: 0.9867 Val AUC: 0.9738 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 260 Train Loss: 0.1524 Acc: 0.9168 Pre: 0.8873 Recall: 0.9550 F1: 0.9199 Train AUC: 0.9843 Val AUC: 0.9720 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 261 Train Loss: 0.1470 Acc: 0.9168 Pre: 0.8915 Recall: 0.9491 F1: 0.9194 Train AUC: 0.9854 Val AUC: 0.9703 Val PRC: 0.9688 Time: 0.24\n",
      "Epoch: 262 Train Loss: 0.1543 Acc: 0.9266 Pre: 0.9022 Recall: 0.9569 F1: 0.9288 Train AUC: 0.9838 Val AUC: 0.9725 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 263 Train Loss: 0.1468 Acc: 0.9207 Pre: 0.8967 Recall: 0.9511 F1: 0.9231 Train AUC: 0.9852 Val AUC: 0.9738 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 264 Train Loss: 0.1594 Acc: 0.9188 Pre: 0.8978 Recall: 0.9452 F1: 0.9209 Train AUC: 0.9830 Val AUC: 0.9739 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 265 Train Loss: 0.1431 Acc: 0.9188 Pre: 0.8993 Recall: 0.9432 F1: 0.9207 Train AUC: 0.9860 Val AUC: 0.9734 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 266 Train Loss: 0.1516 Acc: 0.9266 Pre: 0.9208 Recall: 0.9335 F1: 0.9271 Train AUC: 0.9853 Val AUC: 0.9741 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 267 Train Loss: 0.1418 Acc: 0.9188 Pre: 0.8849 Recall: 0.9628 F1: 0.9222 Train AUC: 0.9864 Val AUC: 0.9766 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 268 Train Loss: 0.1483 Acc: 0.9188 Pre: 0.8877 Recall: 0.9589 F1: 0.9219 Train AUC: 0.9856 Val AUC: 0.9723 Val PRC: 0.9726 Time: 0.23\n",
      "Epoch: 269 Train Loss: 0.1456 Acc: 0.9227 Pre: 0.9138 Recall: 0.9335 F1: 0.9235 Train AUC: 0.9857 Val AUC: 0.9737 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 270 Train Loss: 0.1561 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9844 Val AUC: 0.9725 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 271 Train Loss: 0.1562 Acc: 0.9237 Pre: 0.8987 Recall: 0.9550 F1: 0.9260 Train AUC: 0.9833 Val AUC: 0.9736 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 272 Train Loss: 0.1517 Acc: 0.9247 Pre: 0.9306 Recall: 0.9178 F1: 0.9241 Train AUC: 0.9849 Val AUC: 0.9760 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 273 Train Loss: 0.1415 Acc: 0.9276 Pre: 0.9069 Recall: 0.9530 F1: 0.9294 Train AUC: 0.9871 Val AUC: 0.9741 Val PRC: 0.9746 Time: 0.23\n",
      "Epoch: 274 Train Loss: 0.1329 Acc: 0.9168 Pre: 0.8873 Recall: 0.9550 F1: 0.9199 Train AUC: 0.9879 Val AUC: 0.9746 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 275 Train Loss: 0.1523 Acc: 0.9237 Pre: 0.9032 Recall: 0.9491 F1: 0.9256 Train AUC: 0.9832 Val AUC: 0.9698 Val PRC: 0.9676 Time: 0.23\n",
      "Epoch: 276 Train Loss: 0.1362 Acc: 0.9198 Pre: 0.8865 Recall: 0.9628 F1: 0.9231 Train AUC: 0.9872 Val AUC: 0.9723 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 277 Train Loss: 0.1427 Acc: 0.9256 Pre: 0.9223 Recall: 0.9295 F1: 0.9259 Train AUC: 0.9861 Val AUC: 0.9754 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 278 Train Loss: 0.1517 Acc: 0.9227 Pre: 0.9252 Recall: 0.9198 F1: 0.9225 Train AUC: 0.9841 Val AUC: 0.9759 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 279 Train Loss: 0.1395 Acc: 0.9227 Pre: 0.9045 Recall: 0.9452 F1: 0.9244 Train AUC: 0.9864 Val AUC: 0.9745 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 280 Train Loss: 0.1409 Acc: 0.9247 Pre: 0.9033 Recall: 0.9511 F1: 0.9266 Train AUC: 0.9862 Val AUC: 0.9760 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 281 Train Loss: 0.1450 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9851 Val AUC: 0.9751 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 282 Train Loss: 0.1554 Acc: 0.9198 Pre: 0.9009 Recall: 0.9432 F1: 0.9216 Train AUC: 0.9839 Val AUC: 0.9732 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 283 Train Loss: 0.1425 Acc: 0.9100 Pre: 0.9006 Recall: 0.9217 F1: 0.9110 Train AUC: 0.9859 Val AUC: 0.9712 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 284 Train Loss: 0.1549 Acc: 0.9178 Pre: 0.9051 Recall: 0.9335 F1: 0.9191 Train AUC: 0.9831 Val AUC: 0.9717 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 285 Train Loss: 0.1352 Acc: 0.9188 Pre: 0.8934 Recall: 0.9511 F1: 0.9213 Train AUC: 0.9875 Val AUC: 0.9708 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 286 Train Loss: 0.1330 Acc: 0.9207 Pre: 0.8952 Recall: 0.9530 F1: 0.9232 Train AUC: 0.9884 Val AUC: 0.9725 Val PRC: 0.9702 Time: 0.23\n",
      "Epoch: 287 Train Loss: 0.1267 Acc: 0.9178 Pre: 0.8806 Recall: 0.9667 F1: 0.9216 Train AUC: 0.9894 Val AUC: 0.9753 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 288 Train Loss: 0.1390 Acc: 0.9159 Pre: 0.8972 Recall: 0.9393 F1: 0.9178 Train AUC: 0.9873 Val AUC: 0.9726 Val PRC: 0.9722 Time: 0.23\n",
      "Epoch: 289 Train Loss: 0.1358 Acc: 0.9247 Pre: 0.9094 Recall: 0.9432 F1: 0.9260 Train AUC: 0.9865 Val AUC: 0.9726 Val PRC: 0.9694 Time: 0.23\n",
      "Epoch: 290 Train Loss: 0.1242 Acc: 0.9237 Pre: 0.9032 Recall: 0.9491 F1: 0.9256 Train AUC: 0.9893 Val AUC: 0.9738 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 291 Train Loss: 0.1485 Acc: 0.9198 Pre: 0.8950 Recall: 0.9511 F1: 0.9222 Train AUC: 0.9841 Val AUC: 0.9724 Val PRC: 0.9689 Time: 0.23\n",
      "Epoch: 292 Train Loss: 0.1221 Acc: 0.9198 Pre: 0.9165 Recall: 0.9237 F1: 0.9201 Train AUC: 0.9893 Val AUC: 0.9708 Val PRC: 0.9683 Time: 0.23\n",
      "Epoch: 293 Train Loss: 0.1314 Acc: 0.9247 Pre: 0.9222 Recall: 0.9276 F1: 0.9249 Train AUC: 0.9872 Val AUC: 0.9760 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 294 Train Loss: 0.1571 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9873 Val AUC: 0.9776 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 295 Train Loss: 0.1475 Acc: 0.9247 Pre: 0.9323 Recall: 0.9159 F1: 0.9240 Train AUC: 0.9852 Val AUC: 0.9763 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 296 Train Loss: 0.1596 Acc: 0.9295 Pre: 0.9181 Recall: 0.9432 F1: 0.9305 Train AUC: 0.9869 Val AUC: 0.9751 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 297 Train Loss: 0.1234 Acc: 0.9315 Pre: 0.9137 Recall: 0.9530 F1: 0.9330 Train AUC: 0.9893 Val AUC: 0.9748 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 298 Train Loss: 0.1457 Acc: 0.9266 Pre: 0.9067 Recall: 0.9511 F1: 0.9284 Train AUC: 0.9838 Val AUC: 0.9702 Val PRC: 0.9664 Time: 0.24\n",
      "Epoch: 299 Train Loss: 0.1379 Acc: 0.9286 Pre: 0.9132 Recall: 0.9472 F1: 0.9299 Train AUC: 0.9863 Val AUC: 0.9727 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 300 Train Loss: 0.1300 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9883 Val AUC: 0.9749 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 301 Train Loss: 0.1326 Acc: 0.9276 Pre: 0.9243 Recall: 0.9315 F1: 0.9279 Train AUC: 0.9875 Val AUC: 0.9755 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 302 Train Loss: 0.1295 Acc: 0.9237 Pre: 0.8958 Recall: 0.9589 F1: 0.9263 Train AUC: 0.9883 Val AUC: 0.9749 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 303 Train Loss: 0.1453 Acc: 0.9295 Pre: 0.9262 Recall: 0.9335 F1: 0.9298 Train AUC: 0.9850 Val AUC: 0.9740 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 304 Train Loss: 0.1312 Acc: 0.9198 Pre: 0.8980 Recall: 0.9472 F1: 0.9219 Train AUC: 0.9880 Val AUC: 0.9747 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 305 Train Loss: 0.1289 Acc: 0.9159 Pre: 0.8899 Recall: 0.9491 F1: 0.9186 Train AUC: 0.9882 Val AUC: 0.9723 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 306 Train Loss: 0.1264 Acc: 0.9256 Pre: 0.9207 Recall: 0.9315 F1: 0.9261 Train AUC: 0.9883 Val AUC: 0.9756 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 307 Train Loss: 0.1217 Acc: 0.9227 Pre: 0.8816 Recall: 0.9765 F1: 0.9266 Train AUC: 0.9894 Val AUC: 0.9725 Val PRC: 0.9724 Time: 0.23\n",
      "Epoch: 308 Train Loss: 0.1219 Acc: 0.9247 Pre: 0.9357 Recall: 0.9119 F1: 0.9237 Train AUC: 0.9887 Val AUC: 0.9755 Val PRC: 0.9768 Time: 0.23\n",
      "Epoch: 309 Train Loss: 0.1476 Acc: 0.9207 Pre: 0.8952 Recall: 0.9530 F1: 0.9232 Train AUC: 0.9836 Val AUC: 0.9743 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 310 Train Loss: 0.1296 Acc: 0.9256 Pre: 0.9143 Recall: 0.9393 F1: 0.9266 Train AUC: 0.9877 Val AUC: 0.9725 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 311 Train Loss: 0.1297 Acc: 0.9217 Pre: 0.8954 Recall: 0.9550 F1: 0.9242 Train AUC: 0.9875 Val AUC: 0.9733 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 312 Train Loss: 0.1265 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9870 Val AUC: 0.9704 Val PRC: 0.9658 Time: 0.23\n",
      "Epoch: 313 Train Loss: 0.1177 Acc: 0.9266 Pre: 0.9022 Recall: 0.9569 F1: 0.9288 Train AUC: 0.9891 Val AUC: 0.9757 Val PRC: 0.9768 Time: 0.23\n",
      "Epoch: 314 Train Loss: 0.1314 Acc: 0.9129 Pre: 0.8754 Recall: 0.9628 F1: 0.9171 Train AUC: 0.9877 Val AUC: 0.9745 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 315 Train Loss: 0.1240 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9890 Val AUC: 0.9741 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 316 Train Loss: 0.1394 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9853 Val AUC: 0.9730 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 317 Train Loss: 0.1174 Acc: 0.9266 Pre: 0.9241 Recall: 0.9295 F1: 0.9268 Train AUC: 0.9901 Val AUC: 0.9755 Val PRC: 0.9763 Time: 0.27\n",
      "Epoch: 318 Train Loss: 0.1201 Acc: 0.9198 Pre: 0.8783 Recall: 0.9746 F1: 0.9239 Train AUC: 0.9886 Val AUC: 0.9765 Val PRC: 0.9772 Time: 0.24\n",
      "Epoch: 319 Train Loss: 0.1267 Acc: 0.9305 Pre: 0.9280 Recall: 0.9335 F1: 0.9307 Train AUC: 0.9879 Val AUC: 0.9746 Val PRC: 0.9724 Time: 0.23\n",
      "Epoch: 320 Train Loss: 0.1318 Acc: 0.9217 Pre: 0.8925 Recall: 0.9589 F1: 0.9245 Train AUC: 0.9871 Val AUC: 0.9740 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 321 Train Loss: 0.1053 Acc: 0.9256 Pre: 0.9175 Recall: 0.9354 F1: 0.9264 Train AUC: 0.9917 Val AUC: 0.9751 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 322 Train Loss: 0.1144 Acc: 0.9217 Pre: 0.9217 Recall: 0.9217 F1: 0.9217 Train AUC: 0.9908 Val AUC: 0.9736 Val PRC: 0.9743 Time: 0.41\n",
      "Epoch: 323 Train Loss: 0.1224 Acc: 0.9227 Pre: 0.9186 Recall: 0.9276 F1: 0.9231 Train AUC: 0.9882 Val AUC: 0.9735 Val PRC: 0.9718 Time: 0.24\n",
      "Epoch: 324 Train Loss: 0.1284 Acc: 0.9217 Pre: 0.9028 Recall: 0.9452 F1: 0.9235 Train AUC: 0.9875 Val AUC: 0.9767 Val PRC: 0.9774 Time: 0.23\n",
      "Epoch: 325 Train Loss: 0.1369 Acc: 0.9217 Pre: 0.9028 Recall: 0.9452 F1: 0.9235 Train AUC: 0.9862 Val AUC: 0.9744 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 326 Train Loss: 0.1160 Acc: 0.9237 Pre: 0.9270 Recall: 0.9198 F1: 0.9234 Train AUC: 0.9897 Val AUC: 0.9755 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 327 Train Loss: 0.1346 Acc: 0.9188 Pre: 0.9007 Recall: 0.9413 F1: 0.9206 Train AUC: 0.9863 Val AUC: 0.9724 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 328 Train Loss: 0.1176 Acc: 0.9276 Pre: 0.9115 Recall: 0.9472 F1: 0.9290 Train AUC: 0.9894 Val AUC: 0.9731 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 329 Train Loss: 0.1249 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9892 Val AUC: 0.9754 Val PRC: 0.9768 Time: 0.25\n",
      "Epoch: 330 Train Loss: 0.1162 Acc: 0.9247 Pre: 0.9004 Recall: 0.9550 F1: 0.9269 Train AUC: 0.9899 Val AUC: 0.9742 Val PRC: 0.9715 Time: 0.23\n",
      "Epoch: 331 Train Loss: 0.1236 Acc: 0.9266 Pre: 0.9580 Recall: 0.8924 F1: 0.9240 Train AUC: 0.9897 Val AUC: 0.9758 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 332 Train Loss: 0.1235 Acc: 0.9256 Pre: 0.9273 Recall: 0.9237 F1: 0.9255 Train AUC: 0.9886 Val AUC: 0.9753 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 333 Train Loss: 0.1231 Acc: 0.9325 Pre: 0.9154 Recall: 0.9530 F1: 0.9338 Train AUC: 0.9904 Val AUC: 0.9773 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 334 Train Loss: 0.1123 Acc: 0.9305 Pre: 0.9089 Recall: 0.9569 F1: 0.9323 Train AUC: 0.9908 Val AUC: 0.9755 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 335 Train Loss: 0.1531 Acc: 0.9237 Pre: 0.9140 Recall: 0.9354 F1: 0.9246 Train AUC: 0.9878 Val AUC: 0.9741 Val PRC: 0.9728 Time: 0.23\n",
      "Epoch: 336 Train Loss: 0.1264 Acc: 0.9305 Pre: 0.9104 Recall: 0.9550 F1: 0.9322 Train AUC: 0.9884 Val AUC: 0.9758 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 337 Train Loss: 0.1263 Acc: 0.9335 Pre: 0.9251 Recall: 0.9432 F1: 0.9341 Train AUC: 0.9881 Val AUC: 0.9763 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 338 Train Loss: 0.1242 Acc: 0.9305 Pre: 0.9382 Recall: 0.9217 F1: 0.9299 Train AUC: 0.9889 Val AUC: 0.9792 Val PRC: 0.9794 Time: 0.23\n",
      "Epoch: 339 Train Loss: 0.1221 Acc: 0.9286 Pre: 0.9041 Recall: 0.9589 F1: 0.9307 Train AUC: 0.9885 Val AUC: 0.9771 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 340 Train Loss: 0.1206 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9883 Val AUC: 0.9759 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 341 Train Loss: 0.1184 Acc: 0.9237 Pre: 0.9155 Recall: 0.9335 F1: 0.9244 Train AUC: 0.9886 Val AUC: 0.9759 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 342 Train Loss: 0.1187 Acc: 0.9286 Pre: 0.9117 Recall: 0.9491 F1: 0.9300 Train AUC: 0.9896 Val AUC: 0.9777 Val PRC: 0.9789 Time: 0.23\n",
      "Epoch: 343 Train Loss: 0.1160 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9896 Val AUC: 0.9757 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 344 Train Loss: 0.1192 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9887 Val AUC: 0.9735 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 345 Train Loss: 0.1118 Acc: 0.9295 Pre: 0.9434 Recall: 0.9139 F1: 0.9284 Train AUC: 0.9912 Val AUC: 0.9743 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 346 Train Loss: 0.1215 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9888 Val AUC: 0.9744 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 347 Train Loss: 0.1112 Acc: 0.9286 Pre: 0.9244 Recall: 0.9335 F1: 0.9289 Train AUC: 0.9908 Val AUC: 0.9756 Val PRC: 0.9773 Time: 0.23\n",
      "Epoch: 348 Train Loss: 0.1144 Acc: 0.9247 Pre: 0.9323 Recall: 0.9159 F1: 0.9240 Train AUC: 0.9902 Val AUC: 0.9747 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 349 Train Loss: 0.1221 Acc: 0.9325 Pre: 0.9300 Recall: 0.9354 F1: 0.9327 Train AUC: 0.9869 Val AUC: 0.9749 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 350 Train Loss: 0.1273 Acc: 0.9227 Pre: 0.9045 Recall: 0.9452 F1: 0.9244 Train AUC: 0.9876 Val AUC: 0.9742 Val PRC: 0.9746 Time: 0.23\n",
      "Epoch: 351 Train Loss: 0.1294 Acc: 0.9266 Pre: 0.9129 Recall: 0.9432 F1: 0.9278 Train AUC: 0.9869 Val AUC: 0.9764 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 352 Train Loss: 0.1225 Acc: 0.9315 Pre: 0.9232 Recall: 0.9413 F1: 0.9322 Train AUC: 0.9885 Val AUC: 0.9778 Val PRC: 0.9779 Time: 0.24\n",
      "Epoch: 353 Train Loss: 0.1137 Acc: 0.9325 Pre: 0.9170 Recall: 0.9511 F1: 0.9337 Train AUC: 0.9909 Val AUC: 0.9771 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 354 Train Loss: 0.1185 Acc: 0.9266 Pre: 0.9144 Recall: 0.9413 F1: 0.9277 Train AUC: 0.9890 Val AUC: 0.9742 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 355 Train Loss: 0.1389 Acc: 0.9335 Pre: 0.9318 Recall: 0.9354 F1: 0.9336 Train AUC: 0.9898 Val AUC: 0.9791 Val PRC: 0.9799 Time: 0.23\n",
      "Epoch: 356 Train Loss: 0.1172 Acc: 0.9276 Pre: 0.9194 Recall: 0.9374 F1: 0.9283 Train AUC: 0.9895 Val AUC: 0.9756 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 357 Train Loss: 0.1258 Acc: 0.9295 Pre: 0.9134 Recall: 0.9491 F1: 0.9309 Train AUC: 0.9878 Val AUC: 0.9780 Val PRC: 0.9784 Time: 0.23\n",
      "Epoch: 358 Train Loss: 0.1163 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9900 Val AUC: 0.9772 Val PRC: 0.9782 Time: 0.23\n",
      "Epoch: 359 Train Loss: 0.1139 Acc: 0.9247 Pre: 0.9340 Recall: 0.9139 F1: 0.9238 Train AUC: 0.9897 Val AUC: 0.9772 Val PRC: 0.9776 Time: 0.24\n",
      "Epoch: 360 Train Loss: 0.1196 Acc: 0.9207 Pre: 0.9011 Recall: 0.9452 F1: 0.9226 Train AUC: 0.9886 Val AUC: 0.9733 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 361 Train Loss: 0.1201 Acc: 0.9266 Pre: 0.9098 Recall: 0.9472 F1: 0.9281 Train AUC: 0.9887 Val AUC: 0.9751 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 362 Train Loss: 0.1154 Acc: 0.9315 Pre: 0.9455 Recall: 0.9159 F1: 0.9304 Train AUC: 0.9896 Val AUC: 0.9797 Val PRC: 0.9812 Time: 0.23\n",
      "Epoch: 363 Train Loss: 0.0996 Acc: 0.9266 Pre: 0.9037 Recall: 0.9550 F1: 0.9286 Train AUC: 0.9931 Val AUC: 0.9749 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 364 Train Loss: 0.1102 Acc: 0.9266 Pre: 0.9037 Recall: 0.9550 F1: 0.9286 Train AUC: 0.9903 Val AUC: 0.9751 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 365 Train Loss: 0.1136 Acc: 0.9247 Pre: 0.9141 Recall: 0.9374 F1: 0.9256 Train AUC: 0.9898 Val AUC: 0.9739 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 366 Train Loss: 0.1421 Acc: 0.9286 Pre: 0.9041 Recall: 0.9589 F1: 0.9307 Train AUC: 0.9890 Val AUC: 0.9772 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 367 Train Loss: 0.1008 Acc: 0.9295 Pre: 0.9118 Recall: 0.9511 F1: 0.9310 Train AUC: 0.9922 Val AUC: 0.9754 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 368 Train Loss: 0.1087 Acc: 0.9266 Pre: 0.9241 Recall: 0.9295 F1: 0.9268 Train AUC: 0.9910 Val AUC: 0.9756 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 369 Train Loss: 0.1144 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9891 Val AUC: 0.9743 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 370 Train Loss: 0.1097 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9907 Val AUC: 0.9748 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 371 Train Loss: 0.1044 Acc: 0.9247 Pre: 0.9141 Recall: 0.9374 F1: 0.9256 Train AUC: 0.9910 Val AUC: 0.9753 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 372 Train Loss: 0.1191 Acc: 0.9247 Pre: 0.9049 Recall: 0.9491 F1: 0.9265 Train AUC: 0.9895 Val AUC: 0.9752 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 373 Train Loss: 0.1161 Acc: 0.9335 Pre: 0.9171 Recall: 0.9530 F1: 0.9347 Train AUC: 0.9885 Val AUC: 0.9779 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 374 Train Loss: 0.1227 Acc: 0.9286 Pre: 0.9212 Recall: 0.9374 F1: 0.9292 Train AUC: 0.9894 Val AUC: 0.9771 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 375 Train Loss: 0.1039 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9913 Val AUC: 0.9757 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 376 Train Loss: 0.1140 Acc: 0.9207 Pre: 0.8867 Recall: 0.9648 F1: 0.9241 Train AUC: 0.9901 Val AUC: 0.9765 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 377 Train Loss: 0.1156 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9887 Val AUC: 0.9744 Val PRC: 0.9754 Time: 0.23\n",
      "Epoch: 378 Train Loss: 0.1078 Acc: 0.9325 Pre: 0.9300 Recall: 0.9354 F1: 0.9327 Train AUC: 0.9909 Val AUC: 0.9770 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 379 Train Loss: 0.1054 Acc: 0.9276 Pre: 0.9162 Recall: 0.9413 F1: 0.9286 Train AUC: 0.9911 Val AUC: 0.9772 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 380 Train Loss: 0.1237 Acc: 0.9247 Pre: 0.9049 Recall: 0.9491 F1: 0.9265 Train AUC: 0.9877 Val AUC: 0.9781 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 381 Train Loss: 0.1001 Acc: 0.9247 Pre: 0.9141 Recall: 0.9374 F1: 0.9256 Train AUC: 0.9922 Val AUC: 0.9779 Val PRC: 0.9791 Time: 0.23\n",
      "Epoch: 382 Train Loss: 0.1165 Acc: 0.9335 Pre: 0.9404 Recall: 0.9256 F1: 0.9329 Train AUC: 0.9890 Val AUC: 0.9787 Val PRC: 0.9799 Time: 0.24\n",
      "Epoch: 383 Train Loss: 0.1077 Acc: 0.9276 Pre: 0.9293 Recall: 0.9256 F1: 0.9275 Train AUC: 0.9899 Val AUC: 0.9756 Val PRC: 0.9767 Time: 0.28\n",
      "Epoch: 384 Train Loss: 0.0883 Acc: 0.9305 Pre: 0.9264 Recall: 0.9354 F1: 0.9309 Train AUC: 0.9946 Val AUC: 0.9760 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 385 Train Loss: 0.1179 Acc: 0.9335 Pre: 0.9335 Recall: 0.9335 F1: 0.9335 Train AUC: 0.9884 Val AUC: 0.9783 Val PRC: 0.9791 Time: 0.23\n",
      "Epoch: 386 Train Loss: 0.1084 Acc: 0.9286 Pre: 0.9328 Recall: 0.9237 F1: 0.9282 Train AUC: 0.9909 Val AUC: 0.9776 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 387 Train Loss: 0.1088 Acc: 0.9247 Pre: 0.9033 Recall: 0.9511 F1: 0.9266 Train AUC: 0.9907 Val AUC: 0.9791 Val PRC: 0.9803 Time: 0.23\n",
      "Epoch: 388 Train Loss: 0.1193 Acc: 0.9315 Pre: 0.9232 Recall: 0.9413 F1: 0.9322 Train AUC: 0.9877 Val AUC: 0.9753 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 389 Train Loss: 0.1038 Acc: 0.9305 Pre: 0.9453 Recall: 0.9139 F1: 0.9294 Train AUC: 0.9908 Val AUC: 0.9784 Val PRC: 0.9793 Time: 0.24\n",
      "Epoch: 390 Train Loss: 0.1093 Acc: 0.9247 Pre: 0.9004 Recall: 0.9550 F1: 0.9269 Train AUC: 0.9904 Val AUC: 0.9774 Val PRC: 0.9785 Time: 0.23\n",
      "Epoch: 391 Train Loss: 0.1057 Acc: 0.9335 Pre: 0.9203 Recall: 0.9491 F1: 0.9345 Train AUC: 0.9900 Val AUC: 0.9770 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 392 Train Loss: 0.1064 Acc: 0.9354 Pre: 0.9222 Recall: 0.9511 F1: 0.9364 Train AUC: 0.9909 Val AUC: 0.9744 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 393 Train Loss: 0.1023 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9922 Val AUC: 0.9751 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 394 Train Loss: 0.1002 Acc: 0.9325 Pre: 0.9266 Recall: 0.9393 F1: 0.9329 Train AUC: 0.9923 Val AUC: 0.9765 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 395 Train Loss: 0.0961 Acc: 0.9354 Pre: 0.9423 Recall: 0.9276 F1: 0.9349 Train AUC: 0.9925 Val AUC: 0.9775 Val PRC: 0.9791 Time: 0.24\n",
      "Epoch: 396 Train Loss: 0.0886 Acc: 0.9237 Pre: 0.9171 Recall: 0.9315 F1: 0.9243 Train AUC: 0.9935 Val AUC: 0.9753 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 397 Train Loss: 0.1056 Acc: 0.9276 Pre: 0.9146 Recall: 0.9432 F1: 0.9287 Train AUC: 0.9901 Val AUC: 0.9772 Val PRC: 0.9784 Time: 0.23\n",
      "Epoch: 398 Train Loss: 0.1078 Acc: 0.9286 Pre: 0.9101 Recall: 0.9511 F1: 0.9301 Train AUC: 0.9897 Val AUC: 0.9785 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 399 Train Loss: 0.1025 Acc: 0.9266 Pre: 0.9007 Recall: 0.9589 F1: 0.9289 Train AUC: 0.9909 Val AUC: 0.9793 Val PRC: 0.9800 Time: 0.24\n",
      "Epoch: 400 Train Loss: 0.0979 Acc: 0.9374 Pre: 0.9497 Recall: 0.9237 F1: 0.9365 Train AUC: 0.9924 Val AUC: 0.9805 Val PRC: 0.9818 Time: 0.23\n",
      "Epoch: 401 Train Loss: 0.1051 Acc: 0.9276 Pre: 0.9210 Recall: 0.9354 F1: 0.9282 Train AUC: 0.9903 Val AUC: 0.9778 Val PRC: 0.9794 Time: 0.24\n",
      "Epoch: 402 Train Loss: 0.1069 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9899 Val AUC: 0.9772 Val PRC: 0.9758 Time: 0.24\n",
      "Epoch: 403 Train Loss: 0.1086 Acc: 0.9364 Pre: 0.9390 Recall: 0.9335 F1: 0.9362 Train AUC: 0.9900 Val AUC: 0.9796 Val PRC: 0.9795 Time: 0.24\n",
      "Epoch: 404 Train Loss: 0.1058 Acc: 0.9335 Pre: 0.9203 Recall: 0.9491 F1: 0.9345 Train AUC: 0.9903 Val AUC: 0.9809 Val PRC: 0.9812 Time: 0.25\n",
      "Epoch: 405 Train Loss: 0.1142 Acc: 0.9335 Pre: 0.9386 Recall: 0.9276 F1: 0.9331 Train AUC: 0.9887 Val AUC: 0.9815 Val PRC: 0.9812 Time: 0.23\n",
      "Epoch: 406 Train Loss: 0.1099 Acc: 0.9266 Pre: 0.9129 Recall: 0.9432 F1: 0.9278 Train AUC: 0.9887 Val AUC: 0.9771 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 407 Train Loss: 0.1112 Acc: 0.9286 Pre: 0.9011 Recall: 0.9628 F1: 0.9309 Train AUC: 0.9888 Val AUC: 0.9775 Val PRC: 0.9727 Time: 0.23\n",
      "Epoch: 408 Train Loss: 0.1167 Acc: 0.9325 Pre: 0.9218 Recall: 0.9452 F1: 0.9333 Train AUC: 0.9880 Val AUC: 0.9784 Val PRC: 0.9778 Time: 0.24\n",
      "Epoch: 409 Train Loss: 0.1170 Acc: 0.9315 Pre: 0.9366 Recall: 0.9256 F1: 0.9311 Train AUC: 0.9898 Val AUC: 0.9781 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 410 Train Loss: 0.1065 Acc: 0.9384 Pre: 0.9462 Recall: 0.9295 F1: 0.9378 Train AUC: 0.9895 Val AUC: 0.9790 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 411 Train Loss: 0.1403 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9894 Val AUC: 0.9767 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 412 Train Loss: 0.1224 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9902 Val AUC: 0.9758 Val PRC: 0.9770 Time: 0.24\n",
      "Epoch: 413 Train Loss: 0.1100 Acc: 0.9276 Pre: 0.9226 Recall: 0.9335 F1: 0.9280 Train AUC: 0.9913 Val AUC: 0.9750 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 414 Train Loss: 0.1152 Acc: 0.9325 Pre: 0.9368 Recall: 0.9276 F1: 0.9322 Train AUC: 0.9895 Val AUC: 0.9757 Val PRC: 0.9773 Time: 0.23\n",
      "Epoch: 415 Train Loss: 0.1098 Acc: 0.9305 Pre: 0.9198 Recall: 0.9432 F1: 0.9314 Train AUC: 0.9898 Val AUC: 0.9743 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 416 Train Loss: 0.1152 Acc: 0.9315 Pre: 0.9401 Recall: 0.9217 F1: 0.9308 Train AUC: 0.9899 Val AUC: 0.9751 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 417 Train Loss: 0.1137 Acc: 0.9374 Pre: 0.9461 Recall: 0.9276 F1: 0.9368 Train AUC: 0.9887 Val AUC: 0.9790 Val PRC: 0.9798 Time: 0.23\n",
      "Epoch: 418 Train Loss: 0.1036 Acc: 0.9286 Pre: 0.9117 Recall: 0.9491 F1: 0.9300 Train AUC: 0.9916 Val AUC: 0.9776 Val PRC: 0.9789 Time: 0.24\n",
      "Epoch: 419 Train Loss: 0.1007 Acc: 0.9344 Pre: 0.9237 Recall: 0.9472 F1: 0.9353 Train AUC: 0.9906 Val AUC: 0.9793 Val PRC: 0.9807 Time: 0.23\n",
      "Epoch: 420 Train Loss: 0.0917 Acc: 0.9305 Pre: 0.9183 Recall: 0.9452 F1: 0.9315 Train AUC: 0.9926 Val AUC: 0.9748 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 421 Train Loss: 0.1024 Acc: 0.9325 Pre: 0.9234 Recall: 0.9432 F1: 0.9332 Train AUC: 0.9910 Val AUC: 0.9760 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 422 Train Loss: 0.0929 Acc: 0.9354 Pre: 0.9271 Recall: 0.9452 F1: 0.9360 Train AUC: 0.9928 Val AUC: 0.9782 Val PRC: 0.9809 Time: 0.23\n",
      "Epoch: 423 Train Loss: 0.1058 Acc: 0.9354 Pre: 0.9304 Recall: 0.9413 F1: 0.9358 Train AUC: 0.9898 Val AUC: 0.9747 Val PRC: 0.9760 Time: 0.23\n",
      "Epoch: 424 Train Loss: 0.0878 Acc: 0.9305 Pre: 0.9215 Recall: 0.9413 F1: 0.9313 Train AUC: 0.9939 Val AUC: 0.9754 Val PRC: 0.9774 Time: 0.24\n",
      "Epoch: 425 Train Loss: 0.0959 Acc: 0.9393 Pre: 0.9376 Recall: 0.9413 F1: 0.9395 Train AUC: 0.9922 Val AUC: 0.9762 Val PRC: 0.9778 Time: 0.24\n",
      "Epoch: 426 Train Loss: 0.0998 Acc: 0.9354 Pre: 0.9371 Recall: 0.9335 F1: 0.9353 Train AUC: 0.9908 Val AUC: 0.9750 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 427 Train Loss: 0.0940 Acc: 0.9335 Pre: 0.9284 Recall: 0.9393 F1: 0.9339 Train AUC: 0.9926 Val AUC: 0.9755 Val PRC: 0.9760 Time: 0.24\n",
      "Epoch: 428 Train Loss: 0.1041 Acc: 0.9344 Pre: 0.9458 Recall: 0.9217 F1: 0.9336 Train AUC: 0.9901 Val AUC: 0.9769 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 429 Train Loss: 0.1078 Acc: 0.9335 Pre: 0.9352 Recall: 0.9315 F1: 0.9333 Train AUC: 0.9896 Val AUC: 0.9770 Val PRC: 0.9789 Time: 0.23\n",
      "Epoch: 430 Train Loss: 0.1007 Acc: 0.9286 Pre: 0.9101 Recall: 0.9511 F1: 0.9301 Train AUC: 0.9916 Val AUC: 0.9753 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 431 Train Loss: 0.1005 Acc: 0.9315 Pre: 0.9282 Recall: 0.9354 F1: 0.9318 Train AUC: 0.9908 Val AUC: 0.9775 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 432 Train Loss: 0.1201 Acc: 0.9237 Pre: 0.9140 Recall: 0.9354 F1: 0.9246 Train AUC: 0.9911 Val AUC: 0.9750 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 433 Train Loss: 0.0865 Acc: 0.9374 Pre: 0.9461 Recall: 0.9276 F1: 0.9368 Train AUC: 0.9935 Val AUC: 0.9779 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 434 Train Loss: 0.1080 Acc: 0.9295 Pre: 0.9246 Recall: 0.9354 F1: 0.9300 Train AUC: 0.9884 Val AUC: 0.9756 Val PRC: 0.9759 Time: 0.23\n",
      "Epoch: 435 Train Loss: 0.1043 Acc: 0.9335 Pre: 0.9187 Recall: 0.9511 F1: 0.9346 Train AUC: 0.9906 Val AUC: 0.9774 Val PRC: 0.9790 Time: 0.23\n",
      "Epoch: 436 Train Loss: 0.0924 Acc: 0.9403 Pre: 0.9482 Recall: 0.9315 F1: 0.9398 Train AUC: 0.9924 Val AUC: 0.9808 Val PRC: 0.9826 Time: 0.24\n",
      "Epoch: 437 Train Loss: 0.0948 Acc: 0.9423 Pre: 0.9466 Recall: 0.9374 F1: 0.9420 Train AUC: 0.9918 Val AUC: 0.9788 Val PRC: 0.9803 Time: 0.23\n",
      "Epoch: 438 Train Loss: 0.0967 Acc: 0.9384 Pre: 0.9553 Recall: 0.9198 F1: 0.9372 Train AUC: 0.9922 Val AUC: 0.9795 Val PRC: 0.9811 Time: 0.23\n",
      "Epoch: 439 Train Loss: 0.1019 Acc: 0.9335 Pre: 0.9318 Recall: 0.9354 F1: 0.9336 Train AUC: 0.9902 Val AUC: 0.9775 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 440 Train Loss: 0.0947 Acc: 0.9325 Pre: 0.9385 Recall: 0.9256 F1: 0.9320 Train AUC: 0.9923 Val AUC: 0.9770 Val PRC: 0.9793 Time: 0.23\n",
      "Epoch: 441 Train Loss: 0.1092 Acc: 0.9354 Pre: 0.9337 Recall: 0.9374 F1: 0.9355 Train AUC: 0.9903 Val AUC: 0.9777 Val PRC: 0.9800 Time: 0.23\n",
      "Epoch: 442 Train Loss: 0.1018 Acc: 0.9325 Pre: 0.9170 Recall: 0.9511 F1: 0.9337 Train AUC: 0.9907 Val AUC: 0.9788 Val PRC: 0.9799 Time: 0.23\n",
      "Epoch: 443 Train Loss: 0.1024 Acc: 0.9325 Pre: 0.9234 Recall: 0.9432 F1: 0.9332 Train AUC: 0.9900 Val AUC: 0.9810 Val PRC: 0.9823 Time: 0.23\n",
      "Epoch: 444 Train Loss: 0.1281 Acc: 0.9354 Pre: 0.9337 Recall: 0.9374 F1: 0.9355 Train AUC: 0.9895 Val AUC: 0.9787 Val PRC: 0.9793 Time: 0.24\n",
      "Epoch: 445 Train Loss: 0.0985 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9921 Val AUC: 0.9766 Val PRC: 0.9787 Time: 0.24\n",
      "Epoch: 446 Train Loss: 0.0982 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9921 Val AUC: 0.9786 Val PRC: 0.9807 Time: 0.25\n",
      "Epoch: 447 Train Loss: 0.0934 Acc: 0.9335 Pre: 0.9301 Recall: 0.9374 F1: 0.9337 Train AUC: 0.9921 Val AUC: 0.9779 Val PRC: 0.9800 Time: 0.23\n",
      "Epoch: 448 Train Loss: 0.0868 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9939 Val AUC: 0.9776 Val PRC: 0.9796 Time: 0.24\n",
      "Epoch: 449 Train Loss: 0.0960 Acc: 0.9315 Pre: 0.9298 Recall: 0.9335 F1: 0.9316 Train AUC: 0.9913 Val AUC: 0.9791 Val PRC: 0.9813 Time: 0.23\n",
      "Epoch: 450 Train Loss: 0.0961 Acc: 0.9364 Pre: 0.9240 Recall: 0.9511 F1: 0.9373 Train AUC: 0.9903 Val AUC: 0.9780 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 451 Train Loss: 0.0929 Acc: 0.9315 Pre: 0.9121 Recall: 0.9550 F1: 0.9331 Train AUC: 0.9912 Val AUC: 0.9799 Val PRC: 0.9812 Time: 0.23\n",
      "Epoch: 452 Train Loss: 0.1010 Acc: 0.9335 Pre: 0.9268 Recall: 0.9413 F1: 0.9340 Train AUC: 0.9915 Val AUC: 0.9794 Val PRC: 0.9809 Time: 0.23\n",
      "Epoch: 453 Train Loss: 0.0943 Acc: 0.9344 Pre: 0.9336 Recall: 0.9354 F1: 0.9345 Train AUC: 0.9918 Val AUC: 0.9780 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 454 Train Loss: 0.1138 Acc: 0.9325 Pre: 0.9218 Recall: 0.9452 F1: 0.9333 Train AUC: 0.9926 Val AUC: 0.9781 Val PRC: 0.9809 Time: 0.23\n",
      "Epoch: 455 Train Loss: 0.0936 Acc: 0.9325 Pre: 0.9474 Recall: 0.9159 F1: 0.9313 Train AUC: 0.9930 Val AUC: 0.9798 Val PRC: 0.9809 Time: 0.41\n",
      "Epoch: 456 Train Loss: 0.1010 Acc: 0.9266 Pre: 0.9160 Recall: 0.9393 F1: 0.9275 Train AUC: 0.9914 Val AUC: 0.9767 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 457 Train Loss: 0.1052 Acc: 0.9276 Pre: 0.9178 Recall: 0.9393 F1: 0.9284 Train AUC: 0.9899 Val AUC: 0.9758 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 458 Train Loss: 0.0865 Acc: 0.9315 Pre: 0.9384 Recall: 0.9237 F1: 0.9310 Train AUC: 0.9933 Val AUC: 0.9764 Val PRC: 0.9798 Time: 0.23\n",
      "Epoch: 459 Train Loss: 0.0912 Acc: 0.9286 Pre: 0.9363 Recall: 0.9198 F1: 0.9279 Train AUC: 0.9928 Val AUC: 0.9764 Val PRC: 0.9793 Time: 0.23\n",
      "Epoch: 460 Train Loss: 0.1019 Acc: 0.9286 Pre: 0.9328 Recall: 0.9237 F1: 0.9282 Train AUC: 0.9907 Val AUC: 0.9763 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 461 Train Loss: 0.0991 Acc: 0.9354 Pre: 0.9304 Recall: 0.9413 F1: 0.9358 Train AUC: 0.9911 Val AUC: 0.9757 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 462 Train Loss: 0.0859 Acc: 0.9344 Pre: 0.9286 Recall: 0.9413 F1: 0.9349 Train AUC: 0.9935 Val AUC: 0.9763 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 463 Train Loss: 0.0864 Acc: 0.9305 Pre: 0.9348 Recall: 0.9256 F1: 0.9302 Train AUC: 0.9937 Val AUC: 0.9780 Val PRC: 0.9798 Time: 0.23\n",
      "Epoch: 464 Train Loss: 0.0841 Acc: 0.9286 Pre: 0.9071 Recall: 0.9550 F1: 0.9304 Train AUC: 0.9938 Val AUC: 0.9787 Val PRC: 0.9800 Time: 0.24\n",
      "Epoch: 465 Train Loss: 0.0983 Acc: 0.9295 Pre: 0.9295 Recall: 0.9295 F1: 0.9295 Train AUC: 0.9915 Val AUC: 0.9763 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 466 Train Loss: 0.0865 Acc: 0.9305 Pre: 0.9400 Recall: 0.9198 F1: 0.9298 Train AUC: 0.9925 Val AUC: 0.9786 Val PRC: 0.9810 Time: 0.24\n",
      "Epoch: 467 Train Loss: 0.1112 Acc: 0.9344 Pre: 0.9422 Recall: 0.9256 F1: 0.9339 Train AUC: 0.9934 Val AUC: 0.9795 Val PRC: 0.9816 Time: 0.25\n",
      "Epoch: 468 Train Loss: 0.1204 Acc: 0.9247 Pre: 0.9110 Recall: 0.9413 F1: 0.9259 Train AUC: 0.9912 Val AUC: 0.9790 Val PRC: 0.9808 Time: 0.24\n",
      "Epoch: 469 Train Loss: 0.0892 Acc: 0.9286 Pre: 0.9228 Recall: 0.9354 F1: 0.9291 Train AUC: 0.9931 Val AUC: 0.9764 Val PRC: 0.9783 Time: 0.23\n",
      "Epoch: 470 Train Loss: 0.1057 Acc: 0.9325 Pre: 0.9283 Recall: 0.9374 F1: 0.9328 Train AUC: 0.9935 Val AUC: 0.9778 Val PRC: 0.9778 Time: 0.23\n",
      "Epoch: 471 Train Loss: 0.1259 Acc: 0.9325 Pre: 0.9492 Recall: 0.9139 F1: 0.9312 Train AUC: 0.9895 Val AUC: 0.9779 Val PRC: 0.9805 Time: 0.23\n",
      "Epoch: 472 Train Loss: 0.0856 Acc: 0.9335 Pre: 0.9530 Recall: 0.9119 F1: 0.9320 Train AUC: 0.9933 Val AUC: 0.9776 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 473 Train Loss: 0.0916 Acc: 0.9315 Pre: 0.9232 Recall: 0.9413 F1: 0.9322 Train AUC: 0.9915 Val AUC: 0.9800 Val PRC: 0.9820 Time: 0.24\n",
      "Epoch: 474 Train Loss: 0.0938 Acc: 0.9354 Pre: 0.9271 Recall: 0.9452 F1: 0.9360 Train AUC: 0.9915 Val AUC: 0.9810 Val PRC: 0.9819 Time: 0.23\n",
      "Epoch: 475 Train Loss: 0.0954 Acc: 0.9413 Pre: 0.9328 Recall: 0.9511 F1: 0.9419 Train AUC: 0.9920 Val AUC: 0.9822 Val PRC: 0.9846 Time: 0.23\n",
      "Epoch: 476 Train Loss: 0.1033 Acc: 0.9315 Pre: 0.9401 Recall: 0.9217 F1: 0.9308 Train AUC: 0.9899 Val AUC: 0.9791 Val PRC: 0.9799 Time: 0.23\n",
      "Epoch: 477 Train Loss: 0.0907 Acc: 0.9364 Pre: 0.9176 Recall: 0.9589 F1: 0.9378 Train AUC: 0.9929 Val AUC: 0.9783 Val PRC: 0.9792 Time: 0.23\n",
      "Epoch: 478 Train Loss: 0.0806 Acc: 0.9286 Pre: 0.9132 Recall: 0.9472 F1: 0.9299 Train AUC: 0.9931 Val AUC: 0.9788 Val PRC: 0.9801 Time: 0.23\n",
      "Epoch: 479 Train Loss: 0.1196 Acc: 0.9315 Pre: 0.9437 Recall: 0.9178 F1: 0.9306 Train AUC: 0.9913 Val AUC: 0.9788 Val PRC: 0.9804 Time: 0.23\n",
      "Epoch: 480 Train Loss: 0.0894 Acc: 0.9344 Pre: 0.9440 Recall: 0.9237 F1: 0.9337 Train AUC: 0.9922 Val AUC: 0.9769 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 481 Train Loss: 0.0841 Acc: 0.9295 Pre: 0.9229 Recall: 0.9374 F1: 0.9301 Train AUC: 0.9942 Val AUC: 0.9779 Val PRC: 0.9795 Time: 0.23\n",
      "Epoch: 482 Train Loss: 0.0959 Acc: 0.9295 Pre: 0.9181 Recall: 0.9432 F1: 0.9305 Train AUC: 0.9915 Val AUC: 0.9791 Val PRC: 0.9802 Time: 0.24\n",
      "Epoch: 483 Train Loss: 0.0866 Acc: 0.9286 Pre: 0.9011 Recall: 0.9628 F1: 0.9309 Train AUC: 0.9920 Val AUC: 0.9755 Val PRC: 0.9755 Time: 0.24\n",
      "Epoch: 484 Train Loss: 0.0944 Acc: 0.9305 Pre: 0.9247 Recall: 0.9374 F1: 0.9310 Train AUC: 0.9920 Val AUC: 0.9751 Val PRC: 0.9775 Time: 0.23\n",
      "Epoch: 485 Train Loss: 0.1019 Acc: 0.9217 Pre: 0.9089 Recall: 0.9374 F1: 0.9229 Train AUC: 0.9905 Val AUC: 0.9772 Val PRC: 0.9804 Time: 0.24\n",
      "Epoch: 486 Train Loss: 0.0939 Acc: 0.9344 Pre: 0.9237 Recall: 0.9472 F1: 0.9353 Train AUC: 0.9911 Val AUC: 0.9773 Val PRC: 0.9790 Time: 0.24\n",
      "Epoch: 487 Train Loss: 0.0890 Acc: 0.9364 Pre: 0.9373 Recall: 0.9354 F1: 0.9363 Train AUC: 0.9931 Val AUC: 0.9749 Val PRC: 0.9760 Time: 0.24\n",
      "Epoch: 488 Train Loss: 0.0898 Acc: 0.9315 Pre: 0.9455 Recall: 0.9159 F1: 0.9304 Train AUC: 0.9934 Val AUC: 0.9739 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 489 Train Loss: 0.0893 Acc: 0.9295 Pre: 0.9213 Recall: 0.9393 F1: 0.9302 Train AUC: 0.9924 Val AUC: 0.9748 Val PRC: 0.9786 Time: 0.23\n",
      "Epoch: 490 Train Loss: 0.0960 Acc: 0.9315 Pre: 0.9491 Recall: 0.9119 F1: 0.9301 Train AUC: 0.9919 Val AUC: 0.9760 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 491 Train Loss: 0.0945 Acc: 0.9305 Pre: 0.9264 Recall: 0.9354 F1: 0.9309 Train AUC: 0.9922 Val AUC: 0.9773 Val PRC: 0.9796 Time: 0.23\n",
      "Epoch: 492 Train Loss: 0.0825 Acc: 0.9295 Pre: 0.9262 Recall: 0.9335 F1: 0.9298 Train AUC: 0.9936 Val AUC: 0.9741 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 493 Train Loss: 0.0832 Acc: 0.9305 Pre: 0.9400 Recall: 0.9198 F1: 0.9298 Train AUC: 0.9937 Val AUC: 0.9731 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 494 Train Loss: 0.0941 Acc: 0.9335 Pre: 0.9235 Recall: 0.9452 F1: 0.9342 Train AUC: 0.9914 Val AUC: 0.9777 Val PRC: 0.9806 Time: 0.23\n",
      "Epoch: 495 Train Loss: 0.0947 Acc: 0.9305 Pre: 0.9151 Recall: 0.9491 F1: 0.9318 Train AUC: 0.9922 Val AUC: 0.9770 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 496 Train Loss: 0.0892 Acc: 0.9364 Pre: 0.9390 Recall: 0.9335 F1: 0.9362 Train AUC: 0.9927 Val AUC: 0.9765 Val PRC: 0.9797 Time: 0.23\n",
      "Epoch: 497 Train Loss: 0.0788 Acc: 0.9344 Pre: 0.9422 Recall: 0.9256 F1: 0.9339 Train AUC: 0.9946 Val AUC: 0.9764 Val PRC: 0.9787 Time: 0.23\n",
      "Epoch: 498 Train Loss: 0.0890 Acc: 0.9315 Pre: 0.9232 Recall: 0.9413 F1: 0.9322 Train AUC: 0.9916 Val AUC: 0.9765 Val PRC: 0.9789 Time: 0.24\n",
      "Epoch: 499 Train Loss: 0.0972 Acc: 0.9344 Pre: 0.9353 Recall: 0.9335 F1: 0.9344 Train AUC: 0.9896 Val AUC: 0.9725 Val PRC: 0.9664 Time: 0.23\n",
      "Epoch: 500 Train Loss: 0.0845 Acc: 0.9325 Pre: 0.9402 Recall: 0.9237 F1: 0.9319 Train AUC: 0.9930 Val AUC: 0.9745 Val PRC: 0.9760 Time: 0.23\n",
      "Fold: 3 Best Epoch: 475 Val acc: 0.9413 Val Pre: 0.9328 Val Recall: 0.9511 Val F1: 0.9419 Val AUC: 0.9822 Val PRC: 0.9846\n",
      "------this is 4th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6914 Acc: 0.5098 Pre: 0.5050 Recall: 0.9961 F1: 0.6702 Train AUC: 0.5578 Val AUC: 0.5587 Val PRC: 0.5670 Time: 0.24\n",
      "Epoch: 2 Train Loss: 0.7131 Acc: 0.5000 Pre: 0.5000 Recall: 0.9980 F1: 0.6662 Train AUC: 0.4278 Val AUC: 0.4392 Val PRC: 0.4664 Time: 0.24\n",
      "Epoch: 3 Train Loss: 0.6925 Acc: 0.5029 Pre: 0.5015 Recall: 0.9961 F1: 0.6671 Train AUC: 0.5587 Val AUC: 0.5668 Val PRC: 0.5639 Time: 0.24\n",
      "Epoch: 4 Train Loss: 0.6921 Acc: 0.5049 Pre: 0.5025 Recall: 1.0000 F1: 0.6688 Train AUC: 0.5425 Val AUC: 0.5427 Val PRC: 0.5514 Time: 0.23\n",
      "Epoch: 5 Train Loss: 0.6925 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.5474 Val AUC: 0.5573 Val PRC: 0.5589 Time: 0.24\n",
      "Epoch: 6 Train Loss: 0.7029 Acc: 0.5020 Pre: 0.5010 Recall: 0.9961 F1: 0.6667 Train AUC: 0.4790 Val AUC: 0.4788 Val PRC: 0.5130 Time: 0.23\n",
      "Epoch: 7 Train Loss: 0.6964 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.5230 Val AUC: 0.5211 Val PRC: 0.5270 Time: 0.23\n",
      "Epoch: 8 Train Loss: 0.6831 Acc: 0.5313 Pre: 0.5166 Recall: 0.9746 F1: 0.6753 Train AUC: 0.5905 Val AUC: 0.5921 Val PRC: 0.5807 Time: 0.24\n",
      "Epoch: 9 Train Loss: 0.6590 Acc: 0.6477 Pre: 0.6129 Recall: 0.8023 F1: 0.6949 Train AUC: 0.7283 Val AUC: 0.7210 Val PRC: 0.7118 Time: 0.23\n",
      "Epoch: 10 Train Loss: 0.6694 Acc: 0.5137 Pre: 0.5070 Recall: 0.9922 F1: 0.6711 Train AUC: 0.6539 Val AUC: 0.6757 Val PRC: 0.6866 Time: 0.23\n",
      "Epoch: 11 Train Loss: 0.6669 Acc: 0.5509 Pre: 0.5277 Recall: 0.9687 F1: 0.6832 Train AUC: 0.6663 Val AUC: 0.6772 Val PRC: 0.7006 Time: 0.24\n",
      "Epoch: 12 Train Loss: 0.6615 Acc: 0.6213 Pre: 0.5878 Recall: 0.8121 F1: 0.6820 Train AUC: 0.6900 Val AUC: 0.6959 Val PRC: 0.6827 Time: 0.24\n",
      "Epoch: 13 Train Loss: 0.6442 Acc: 0.6556 Pre: 0.6042 Recall: 0.9022 F1: 0.7237 Train AUC: 0.7476 Val AUC: 0.7551 Val PRC: 0.7454 Time: 0.24\n",
      "Epoch: 14 Train Loss: 0.6228 Acc: 0.7603 Pre: 0.7350 Recall: 0.8141 F1: 0.7725 Train AUC: 0.8207 Val AUC: 0.8180 Val PRC: 0.8006 Time: 0.23\n",
      "Epoch: 15 Train Loss: 0.6009 Acc: 0.7613 Pre: 0.7459 Recall: 0.7926 F1: 0.7685 Train AUC: 0.8519 Val AUC: 0.8373 Val PRC: 0.8322 Time: 0.24\n",
      "Epoch: 16 Train Loss: 0.6222 Acc: 0.6840 Pre: 0.6208 Recall: 0.9452 F1: 0.7494 Train AUC: 0.7710 Val AUC: 0.7680 Val PRC: 0.7607 Time: 0.24\n",
      "Epoch: 17 Train Loss: 0.6065 Acc: 0.7339 Pre: 0.6648 Recall: 0.9432 F1: 0.7799 Train AUC: 0.8225 Val AUC: 0.8147 Val PRC: 0.7943 Time: 0.23\n",
      "Epoch: 18 Train Loss: 0.6061 Acc: 0.6967 Pre: 0.6338 Recall: 0.9315 F1: 0.7544 Train AUC: 0.7996 Val AUC: 0.7973 Val PRC: 0.7866 Time: 0.23\n",
      "Epoch: 19 Train Loss: 0.5894 Acc: 0.7554 Pre: 0.6998 Recall: 0.8943 F1: 0.7852 Train AUC: 0.8255 Val AUC: 0.8327 Val PRC: 0.8187 Time: 0.23\n",
      "Epoch: 20 Train Loss: 0.5851 Acc: 0.7505 Pre: 0.6844 Recall: 0.9295 F1: 0.7884 Train AUC: 0.8353 Val AUC: 0.8276 Val PRC: 0.8090 Time: 0.23\n",
      "Epoch: 21 Train Loss: 0.5809 Acc: 0.7476 Pre: 0.6789 Recall: 0.9393 F1: 0.7882 Train AUC: 0.8198 Val AUC: 0.8215 Val PRC: 0.7986 Time: 0.23\n",
      "Epoch: 22 Train Loss: 0.5701 Acc: 0.7544 Pre: 0.6890 Recall: 0.9276 F1: 0.7907 Train AUC: 0.8361 Val AUC: 0.8353 Val PRC: 0.8121 Time: 0.23\n",
      "Epoch: 23 Train Loss: 0.5434 Acc: 0.7916 Pre: 0.7508 Recall: 0.8728 F1: 0.8072 Train AUC: 0.8747 Val AUC: 0.8747 Val PRC: 0.8586 Time: 0.24\n",
      "Epoch: 24 Train Loss: 0.5263 Acc: 0.7975 Pre: 0.7420 Recall: 0.9119 F1: 0.8183 Train AUC: 0.8872 Val AUC: 0.8893 Val PRC: 0.8792 Time: 0.24\n",
      "Epoch: 25 Train Loss: 0.5142 Acc: 0.8180 Pre: 0.8310 Recall: 0.7984 F1: 0.8144 Train AUC: 0.8934 Val AUC: 0.8954 Val PRC: 0.8777 Time: 0.23\n",
      "Epoch: 26 Train Loss: 0.5124 Acc: 0.8200 Pre: 0.7843 Recall: 0.8826 F1: 0.8306 Train AUC: 0.8885 Val AUC: 0.9021 Val PRC: 0.8903 Time: 0.23\n",
      "Epoch: 27 Train Loss: 0.5056 Acc: 0.8278 Pre: 0.8178 Recall: 0.8434 F1: 0.8304 Train AUC: 0.8958 Val AUC: 0.9045 Val PRC: 0.8919 Time: 0.24\n",
      "Epoch: 28 Train Loss: 0.4920 Acc: 0.8229 Pre: 0.7905 Recall: 0.8787 F1: 0.8323 Train AUC: 0.9024 Val AUC: 0.9061 Val PRC: 0.9006 Time: 0.23\n",
      "Epoch: 29 Train Loss: 0.4652 Acc: 0.8288 Pre: 0.7937 Recall: 0.8885 F1: 0.8384 Train AUC: 0.9096 Val AUC: 0.9153 Val PRC: 0.9131 Time: 0.23\n",
      "Epoch: 30 Train Loss: 0.4532 Acc: 0.8493 Pre: 0.8680 Recall: 0.8239 F1: 0.8454 Train AUC: 0.9106 Val AUC: 0.9170 Val PRC: 0.9201 Time: 0.23\n",
      "Epoch: 31 Train Loss: 0.4416 Acc: 0.8415 Pre: 0.8525 Recall: 0.8258 F1: 0.8390 Train AUC: 0.9171 Val AUC: 0.9133 Val PRC: 0.9173 Time: 0.23\n",
      "Epoch: 32 Train Loss: 0.4253 Acc: 0.8464 Pre: 0.8612 Recall: 0.8258 F1: 0.8432 Train AUC: 0.9189 Val AUC: 0.9164 Val PRC: 0.9138 Time: 0.23\n",
      "Epoch: 33 Train Loss: 0.4048 Acc: 0.8434 Pre: 0.8944 Recall: 0.7789 F1: 0.8326 Train AUC: 0.9208 Val AUC: 0.9195 Val PRC: 0.9288 Time: 0.23\n",
      "Epoch: 34 Train Loss: 0.3968 Acc: 0.8532 Pre: 0.8950 Recall: 0.8004 F1: 0.8450 Train AUC: 0.9189 Val AUC: 0.9245 Val PRC: 0.9272 Time: 0.24\n",
      "Epoch: 35 Train Loss: 0.3903 Acc: 0.8278 Pre: 0.7975 Recall: 0.8787 F1: 0.8361 Train AUC: 0.9167 Val AUC: 0.9228 Val PRC: 0.9306 Time: 0.23\n",
      "Epoch: 36 Train Loss: 0.3685 Acc: 0.8503 Pre: 0.8978 Recall: 0.7906 F1: 0.8408 Train AUC: 0.9253 Val AUC: 0.9232 Val PRC: 0.9305 Time: 0.23\n",
      "Epoch: 37 Train Loss: 0.3728 Acc: 0.8571 Pre: 0.9047 Recall: 0.7984 F1: 0.8482 Train AUC: 0.9220 Val AUC: 0.9222 Val PRC: 0.9275 Time: 0.23\n",
      "Epoch: 38 Train Loss: 0.3613 Acc: 0.8552 Pre: 0.8954 Recall: 0.8043 F1: 0.8474 Train AUC: 0.9212 Val AUC: 0.9256 Val PRC: 0.9346 Time: 0.23\n",
      "Epoch: 39 Train Loss: 0.3401 Acc: 0.8650 Pre: 0.9268 Recall: 0.7926 F1: 0.8544 Train AUC: 0.9295 Val AUC: 0.9304 Val PRC: 0.9350 Time: 0.23\n",
      "Epoch: 40 Train Loss: 0.3470 Acc: 0.8493 Pre: 0.8592 Recall: 0.8356 F1: 0.8472 Train AUC: 0.9228 Val AUC: 0.9283 Val PRC: 0.9370 Time: 0.23\n",
      "Epoch: 41 Train Loss: 0.3400 Acc: 0.8611 Pre: 0.9073 Recall: 0.8043 F1: 0.8527 Train AUC: 0.9265 Val AUC: 0.9263 Val PRC: 0.9357 Time: 0.23\n",
      "Epoch: 42 Train Loss: 0.3365 Acc: 0.8532 Pre: 0.8753 Recall: 0.8239 F1: 0.8488 Train AUC: 0.9267 Val AUC: 0.9288 Val PRC: 0.9366 Time: 0.23\n",
      "Epoch: 43 Train Loss: 0.3129 Acc: 0.8611 Pre: 0.9128 Recall: 0.7984 F1: 0.8518 Train AUC: 0.9361 Val AUC: 0.9298 Val PRC: 0.9400 Time: 0.24\n",
      "Epoch: 44 Train Loss: 0.3204 Acc: 0.8630 Pre: 0.8955 Recall: 0.8219 F1: 0.8571 Train AUC: 0.9330 Val AUC: 0.9317 Val PRC: 0.9390 Time: 0.23\n",
      "Epoch: 45 Train Loss: 0.3191 Acc: 0.8454 Pre: 0.8275 Recall: 0.8728 F1: 0.8495 Train AUC: 0.9329 Val AUC: 0.9306 Val PRC: 0.9394 Time: 0.24\n",
      "Epoch: 46 Train Loss: 0.3148 Acc: 0.8562 Pre: 0.8714 Recall: 0.8356 F1: 0.8531 Train AUC: 0.9326 Val AUC: 0.9296 Val PRC: 0.9359 Time: 0.24\n",
      "Epoch: 47 Train Loss: 0.3043 Acc: 0.8513 Pre: 0.9145 Recall: 0.7750 F1: 0.8390 Train AUC: 0.9390 Val AUC: 0.9272 Val PRC: 0.9346 Time: 0.24\n",
      "Epoch: 48 Train Loss: 0.3170 Acc: 0.8601 Pre: 0.8882 Recall: 0.8239 F1: 0.8548 Train AUC: 0.9326 Val AUC: 0.9343 Val PRC: 0.9423 Time: 0.24\n",
      "Epoch: 49 Train Loss: 0.3157 Acc: 0.8620 Pre: 0.9302 Recall: 0.7828 F1: 0.8502 Train AUC: 0.9342 Val AUC: 0.9337 Val PRC: 0.9419 Time: 0.24\n",
      "Epoch: 50 Train Loss: 0.2970 Acc: 0.8650 Pre: 0.8723 Recall: 0.8552 F1: 0.8636 Train AUC: 0.9411 Val AUC: 0.9375 Val PRC: 0.9385 Time: 0.24\n",
      "Epoch: 51 Train Loss: 0.3008 Acc: 0.8630 Pre: 0.8922 Recall: 0.8258 F1: 0.8577 Train AUC: 0.9386 Val AUC: 0.9310 Val PRC: 0.9405 Time: 0.23\n",
      "Epoch: 52 Train Loss: 0.3035 Acc: 0.8581 Pre: 0.9013 Recall: 0.8043 F1: 0.8501 Train AUC: 0.9391 Val AUC: 0.9318 Val PRC: 0.9391 Time: 0.24\n",
      "Epoch: 53 Train Loss: 0.2992 Acc: 0.8669 Pre: 0.9085 Recall: 0.8160 F1: 0.8598 Train AUC: 0.9424 Val AUC: 0.9343 Val PRC: 0.9424 Time: 0.24\n",
      "Epoch: 54 Train Loss: 0.2970 Acc: 0.8523 Pre: 0.8502 Recall: 0.8552 F1: 0.8527 Train AUC: 0.9435 Val AUC: 0.9388 Val PRC: 0.9419 Time: 0.23\n",
      "Epoch: 55 Train Loss: 0.2965 Acc: 0.8630 Pre: 0.8793 Recall: 0.8415 F1: 0.8600 Train AUC: 0.9422 Val AUC: 0.9413 Val PRC: 0.9476 Time: 0.23\n",
      "Epoch: 56 Train Loss: 0.2971 Acc: 0.8708 Pre: 0.8891 Recall: 0.8474 F1: 0.8677 Train AUC: 0.9430 Val AUC: 0.9395 Val PRC: 0.9449 Time: 0.23\n",
      "Epoch: 57 Train Loss: 0.2878 Acc: 0.8650 Pre: 0.8410 Recall: 0.9002 F1: 0.8696 Train AUC: 0.9463 Val AUC: 0.9421 Val PRC: 0.9493 Time: 0.23\n",
      "Epoch: 58 Train Loss: 0.2857 Acc: 0.8562 Pre: 0.8421 Recall: 0.8767 F1: 0.8591 Train AUC: 0.9474 Val AUC: 0.9410 Val PRC: 0.9470 Time: 0.23\n",
      "Epoch: 59 Train Loss: 0.2650 Acc: 0.8679 Pre: 0.8730 Recall: 0.8611 F1: 0.8670 Train AUC: 0.9544 Val AUC: 0.9432 Val PRC: 0.9478 Time: 0.23\n",
      "Epoch: 60 Train Loss: 0.2845 Acc: 0.8767 Pre: 0.8905 Recall: 0.8591 F1: 0.8745 Train AUC: 0.9467 Val AUC: 0.9419 Val PRC: 0.9497 Time: 0.23\n",
      "Epoch: 61 Train Loss: 0.2835 Acc: 0.8611 Pre: 0.8653 Recall: 0.8552 F1: 0.8602 Train AUC: 0.9469 Val AUC: 0.9402 Val PRC: 0.9418 Time: 0.24\n",
      "Epoch: 62 Train Loss: 0.2765 Acc: 0.8611 Pre: 0.8423 Recall: 0.8885 F1: 0.8648 Train AUC: 0.9535 Val AUC: 0.9450 Val PRC: 0.9510 Time: 0.24\n",
      "Epoch: 63 Train Loss: 0.2825 Acc: 0.8591 Pre: 0.8577 Recall: 0.8611 F1: 0.8594 Train AUC: 0.9495 Val AUC: 0.9424 Val PRC: 0.9469 Time: 0.23\n",
      "Epoch: 64 Train Loss: 0.2748 Acc: 0.8640 Pre: 0.9227 Recall: 0.7945 F1: 0.8538 Train AUC: 0.9513 Val AUC: 0.9364 Val PRC: 0.9364 Time: 0.23\n",
      "Epoch: 65 Train Loss: 0.2664 Acc: 0.8542 Pre: 0.8364 Recall: 0.8806 F1: 0.8580 Train AUC: 0.9551 Val AUC: 0.9400 Val PRC: 0.9442 Time: 0.24\n",
      "Epoch: 66 Train Loss: 0.2791 Acc: 0.8689 Pre: 0.8471 Recall: 0.9002 F1: 0.8729 Train AUC: 0.9483 Val AUC: 0.9481 Val PRC: 0.9529 Time: 0.24\n",
      "Epoch: 67 Train Loss: 0.2828 Acc: 0.8718 Pre: 0.8800 Recall: 0.8611 F1: 0.8704 Train AUC: 0.9492 Val AUC: 0.9435 Val PRC: 0.9495 Time: 0.25\n",
      "Epoch: 68 Train Loss: 0.2797 Acc: 0.8777 Pre: 0.8829 Recall: 0.8708 F1: 0.8768 Train AUC: 0.9508 Val AUC: 0.9467 Val PRC: 0.9452 Time: 0.25\n",
      "Epoch: 69 Train Loss: 0.2676 Acc: 0.8659 Pre: 0.8528 Recall: 0.8845 F1: 0.8684 Train AUC: 0.9540 Val AUC: 0.9453 Val PRC: 0.9395 Time: 0.24\n",
      "Epoch: 70 Train Loss: 0.2603 Acc: 0.8757 Pre: 0.8855 Recall: 0.8630 F1: 0.8741 Train AUC: 0.9578 Val AUC: 0.9476 Val PRC: 0.9521 Time: 0.23\n",
      "Epoch: 71 Train Loss: 0.2634 Acc: 0.8836 Pre: 0.9050 Recall: 0.8571 F1: 0.8804 Train AUC: 0.9562 Val AUC: 0.9511 Val PRC: 0.9474 Time: 0.24\n",
      "Epoch: 72 Train Loss: 0.2650 Acc: 0.8699 Pre: 0.8566 Recall: 0.8885 F1: 0.8722 Train AUC: 0.9553 Val AUC: 0.9488 Val PRC: 0.9516 Time: 0.23\n",
      "Epoch: 73 Train Loss: 0.2666 Acc: 0.8718 Pre: 0.8740 Recall: 0.8689 F1: 0.8714 Train AUC: 0.9548 Val AUC: 0.9475 Val PRC: 0.9494 Time: 0.24\n",
      "Epoch: 74 Train Loss: 0.2644 Acc: 0.8699 Pre: 0.8735 Recall: 0.8650 F1: 0.8692 Train AUC: 0.9565 Val AUC: 0.9471 Val PRC: 0.9501 Time: 0.23\n",
      "Epoch: 75 Train Loss: 0.2536 Acc: 0.8767 Pre: 0.8681 Recall: 0.8885 F1: 0.8781 Train AUC: 0.9595 Val AUC: 0.9505 Val PRC: 0.9514 Time: 0.23\n",
      "Epoch: 76 Train Loss: 0.2612 Acc: 0.8767 Pre: 0.8937 Recall: 0.8552 F1: 0.8740 Train AUC: 0.9568 Val AUC: 0.9524 Val PRC: 0.9546 Time: 0.23\n",
      "Epoch: 77 Train Loss: 0.2602 Acc: 0.8748 Pre: 0.8593 Recall: 0.8963 F1: 0.8774 Train AUC: 0.9581 Val AUC: 0.9519 Val PRC: 0.9494 Time: 0.23\n",
      "Epoch: 78 Train Loss: 0.2613 Acc: 0.8738 Pre: 0.8659 Recall: 0.8845 F1: 0.8751 Train AUC: 0.9579 Val AUC: 0.9526 Val PRC: 0.9541 Time: 0.24\n",
      "Epoch: 79 Train Loss: 0.2576 Acc: 0.8708 Pre: 0.8284 Recall: 0.9354 F1: 0.8787 Train AUC: 0.9601 Val AUC: 0.9552 Val PRC: 0.9506 Time: 0.23\n",
      "Epoch: 80 Train Loss: 0.2547 Acc: 0.8865 Pre: 0.9072 Recall: 0.8611 F1: 0.8835 Train AUC: 0.9611 Val AUC: 0.9525 Val PRC: 0.9480 Time: 0.24\n",
      "Epoch: 81 Train Loss: 0.2627 Acc: 0.8777 Pre: 0.8829 Recall: 0.8708 F1: 0.8768 Train AUC: 0.9584 Val AUC: 0.9521 Val PRC: 0.9553 Time: 0.24\n",
      "Epoch: 82 Train Loss: 0.2496 Acc: 0.8777 Pre: 0.8535 Recall: 0.9119 F1: 0.8817 Train AUC: 0.9618 Val AUC: 0.9527 Val PRC: 0.9492 Time: 0.24\n",
      "Epoch: 83 Train Loss: 0.2521 Acc: 0.8845 Pre: 0.8687 Recall: 0.9061 F1: 0.8870 Train AUC: 0.9611 Val AUC: 0.9554 Val PRC: 0.9562 Time: 0.23\n",
      "Epoch: 84 Train Loss: 0.2403 Acc: 0.8865 Pre: 0.8865 Recall: 0.8865 F1: 0.8865 Train AUC: 0.9650 Val AUC: 0.9553 Val PRC: 0.9515 Time: 0.23\n",
      "Epoch: 85 Train Loss: 0.2454 Acc: 0.8865 Pre: 0.8664 Recall: 0.9139 F1: 0.8895 Train AUC: 0.9636 Val AUC: 0.9567 Val PRC: 0.9567 Time: 0.41\n",
      "Epoch: 86 Train Loss: 0.2403 Acc: 0.8806 Pre: 0.8430 Recall: 0.9354 F1: 0.8868 Train AUC: 0.9647 Val AUC: 0.9565 Val PRC: 0.9530 Time: 0.24\n",
      "Epoch: 87 Train Loss: 0.2442 Acc: 0.8796 Pre: 0.8489 Recall: 0.9237 F1: 0.8847 Train AUC: 0.9630 Val AUC: 0.9576 Val PRC: 0.9537 Time: 0.23\n",
      "Epoch: 88 Train Loss: 0.2432 Acc: 0.8953 Pre: 0.8885 Recall: 0.9041 F1: 0.8962 Train AUC: 0.9639 Val AUC: 0.9587 Val PRC: 0.9559 Time: 0.23\n",
      "Epoch: 89 Train Loss: 0.2401 Acc: 0.8885 Pre: 0.8810 Recall: 0.8982 F1: 0.8895 Train AUC: 0.9647 Val AUC: 0.9573 Val PRC: 0.9531 Time: 0.24\n",
      "Epoch: 90 Train Loss: 0.2383 Acc: 0.8933 Pre: 0.9004 Recall: 0.8845 F1: 0.8924 Train AUC: 0.9652 Val AUC: 0.9551 Val PRC: 0.9534 Time: 0.23\n",
      "Epoch: 91 Train Loss: 0.2487 Acc: 0.8885 Pre: 0.8795 Recall: 0.9002 F1: 0.8897 Train AUC: 0.9637 Val AUC: 0.9573 Val PRC: 0.9527 Time: 0.23\n",
      "Epoch: 92 Train Loss: 0.2426 Acc: 0.8826 Pre: 0.8738 Recall: 0.8943 F1: 0.8839 Train AUC: 0.9643 Val AUC: 0.9560 Val PRC: 0.9500 Time: 0.23\n",
      "Epoch: 93 Train Loss: 0.2301 Acc: 0.8796 Pre: 0.8489 Recall: 0.9237 F1: 0.8847 Train AUC: 0.9674 Val AUC: 0.9557 Val PRC: 0.9505 Time: 0.23\n",
      "Epoch: 94 Train Loss: 0.2360 Acc: 0.8894 Pre: 0.8948 Recall: 0.8826 F1: 0.8887 Train AUC: 0.9670 Val AUC: 0.9559 Val PRC: 0.9555 Time: 0.23\n",
      "Epoch: 95 Train Loss: 0.2380 Acc: 0.8904 Pre: 0.8815 Recall: 0.9022 F1: 0.8917 Train AUC: 0.9659 Val AUC: 0.9564 Val PRC: 0.9563 Time: 0.24\n",
      "Epoch: 96 Train Loss: 0.2249 Acc: 0.8855 Pre: 0.9053 Recall: 0.8611 F1: 0.8826 Train AUC: 0.9689 Val AUC: 0.9563 Val PRC: 0.9520 Time: 0.23\n",
      "Epoch: 97 Train Loss: 0.2515 Acc: 0.8836 Pre: 0.8712 Recall: 0.9002 F1: 0.8855 Train AUC: 0.9624 Val AUC: 0.9575 Val PRC: 0.9471 Time: 0.23\n",
      "Epoch: 98 Train Loss: 0.2503 Acc: 0.8757 Pre: 0.8542 Recall: 0.9061 F1: 0.8794 Train AUC: 0.9632 Val AUC: 0.9558 Val PRC: 0.9507 Time: 0.24\n",
      "Epoch: 99 Train Loss: 0.2345 Acc: 0.8806 Pre: 0.8517 Recall: 0.9217 F1: 0.8853 Train AUC: 0.9669 Val AUC: 0.9591 Val PRC: 0.9549 Time: 0.23\n",
      "Epoch: 100 Train Loss: 0.2358 Acc: 0.8914 Pre: 0.8984 Recall: 0.8826 F1: 0.8904 Train AUC: 0.9675 Val AUC: 0.9549 Val PRC: 0.9525 Time: 0.23\n",
      "Epoch: 101 Train Loss: 0.2490 Acc: 0.8777 Pre: 0.8386 Recall: 0.9354 F1: 0.8844 Train AUC: 0.9629 Val AUC: 0.9548 Val PRC: 0.9526 Time: 0.23\n",
      "Epoch: 102 Train Loss: 0.2320 Acc: 0.8885 Pre: 0.8946 Recall: 0.8806 F1: 0.8876 Train AUC: 0.9674 Val AUC: 0.9597 Val PRC: 0.9556 Time: 0.23\n",
      "Epoch: 103 Train Loss: 0.2450 Acc: 0.8875 Pre: 0.8511 Recall: 0.9393 F1: 0.8930 Train AUC: 0.9642 Val AUC: 0.9572 Val PRC: 0.9476 Time: 0.23\n",
      "Epoch: 104 Train Loss: 0.2432 Acc: 0.8875 Pre: 0.8600 Recall: 0.9256 F1: 0.8916 Train AUC: 0.9648 Val AUC: 0.9577 Val PRC: 0.9564 Time: 0.23\n",
      "Epoch: 105 Train Loss: 0.2323 Acc: 0.8953 Pre: 0.8826 Recall: 0.9119 F1: 0.8970 Train AUC: 0.9677 Val AUC: 0.9596 Val PRC: 0.9531 Time: 0.23\n",
      "Epoch: 106 Train Loss: 0.2305 Acc: 0.8885 Pre: 0.8656 Recall: 0.9198 F1: 0.8918 Train AUC: 0.9680 Val AUC: 0.9588 Val PRC: 0.9569 Time: 0.23\n",
      "Epoch: 107 Train Loss: 0.2271 Acc: 0.8904 Pre: 0.9014 Recall: 0.8767 F1: 0.8889 Train AUC: 0.9699 Val AUC: 0.9570 Val PRC: 0.9562 Time: 0.24\n",
      "Epoch: 108 Train Loss: 0.2385 Acc: 0.8924 Pre: 0.8776 Recall: 0.9119 F1: 0.8944 Train AUC: 0.9657 Val AUC: 0.9599 Val PRC: 0.9599 Time: 0.23\n",
      "Epoch: 109 Train Loss: 0.2300 Acc: 0.8875 Pre: 0.8626 Recall: 0.9217 F1: 0.8912 Train AUC: 0.9687 Val AUC: 0.9564 Val PRC: 0.9512 Time: 0.24\n",
      "Epoch: 110 Train Loss: 0.2315 Acc: 0.8943 Pre: 0.8943 Recall: 0.8943 F1: 0.8943 Train AUC: 0.9678 Val AUC: 0.9597 Val PRC: 0.9580 Time: 0.24\n",
      "Epoch: 111 Train Loss: 0.2220 Acc: 0.8894 Pre: 0.8685 Recall: 0.9178 F1: 0.8925 Train AUC: 0.9700 Val AUC: 0.9584 Val PRC: 0.9542 Time: 0.23\n",
      "Epoch: 112 Train Loss: 0.2306 Acc: 0.8933 Pre: 0.8836 Recall: 0.9061 F1: 0.8947 Train AUC: 0.9679 Val AUC: 0.9600 Val PRC: 0.9603 Time: 0.24\n",
      "Epoch: 113 Train Loss: 0.2172 Acc: 0.8943 Pre: 0.8868 Recall: 0.9041 F1: 0.8953 Train AUC: 0.9715 Val AUC: 0.9603 Val PRC: 0.9576 Time: 0.23\n",
      "Epoch: 114 Train Loss: 0.2171 Acc: 0.8904 Pre: 0.8688 Recall: 0.9198 F1: 0.8935 Train AUC: 0.9714 Val AUC: 0.9594 Val PRC: 0.9563 Time: 0.23\n",
      "Epoch: 115 Train Loss: 0.2248 Acc: 0.8914 Pre: 0.8774 Recall: 0.9100 F1: 0.8934 Train AUC: 0.9691 Val AUC: 0.9606 Val PRC: 0.9552 Time: 0.23\n",
      "Epoch: 116 Train Loss: 0.2152 Acc: 0.8943 Pre: 0.8657 Recall: 0.9335 F1: 0.8983 Train AUC: 0.9721 Val AUC: 0.9586 Val PRC: 0.9538 Time: 0.23\n",
      "Epoch: 117 Train Loss: 0.2304 Acc: 0.8933 Pre: 0.8792 Recall: 0.9119 F1: 0.8953 Train AUC: 0.9677 Val AUC: 0.9625 Val PRC: 0.9633 Time: 0.23\n",
      "Epoch: 118 Train Loss: 0.2169 Acc: 0.8865 Pre: 0.8571 Recall: 0.9276 F1: 0.8910 Train AUC: 0.9718 Val AUC: 0.9595 Val PRC: 0.9601 Time: 0.24\n",
      "Epoch: 119 Train Loss: 0.2163 Acc: 0.8894 Pre: 0.8672 Recall: 0.9198 F1: 0.8927 Train AUC: 0.9715 Val AUC: 0.9590 Val PRC: 0.9548 Time: 0.23\n",
      "Epoch: 120 Train Loss: 0.2177 Acc: 0.8933 Pre: 0.8880 Recall: 0.9002 F1: 0.8941 Train AUC: 0.9715 Val AUC: 0.9609 Val PRC: 0.9608 Time: 0.23\n",
      "Epoch: 121 Train Loss: 0.2230 Acc: 0.8953 Pre: 0.8855 Recall: 0.9080 F1: 0.8966 Train AUC: 0.9694 Val AUC: 0.9603 Val PRC: 0.9539 Time: 0.23\n",
      "Epoch: 122 Train Loss: 0.2153 Acc: 0.8963 Pre: 0.9108 Recall: 0.8787 F1: 0.8944 Train AUC: 0.9720 Val AUC: 0.9586 Val PRC: 0.9533 Time: 0.23\n",
      "Epoch: 123 Train Loss: 0.2242 Acc: 0.8982 Pre: 0.8967 Recall: 0.9002 F1: 0.8984 Train AUC: 0.9701 Val AUC: 0.9624 Val PRC: 0.9591 Time: 0.23\n",
      "Epoch: 124 Train Loss: 0.2140 Acc: 0.9012 Pre: 0.8897 Recall: 0.9159 F1: 0.9026 Train AUC: 0.9722 Val AUC: 0.9593 Val PRC: 0.9547 Time: 0.23\n",
      "Epoch: 125 Train Loss: 0.2151 Acc: 0.8973 Pre: 0.8919 Recall: 0.9041 F1: 0.8980 Train AUC: 0.9720 Val AUC: 0.9603 Val PRC: 0.9555 Time: 0.23\n",
      "Epoch: 126 Train Loss: 0.2062 Acc: 0.9022 Pre: 0.8929 Recall: 0.9139 F1: 0.9033 Train AUC: 0.9743 Val AUC: 0.9618 Val PRC: 0.9580 Time: 0.23\n",
      "Epoch: 127 Train Loss: 0.2140 Acc: 0.8963 Pre: 0.8828 Recall: 0.9139 F1: 0.8981 Train AUC: 0.9722 Val AUC: 0.9593 Val PRC: 0.9546 Time: 0.24\n",
      "Epoch: 128 Train Loss: 0.2188 Acc: 0.8992 Pre: 0.8750 Recall: 0.9315 F1: 0.9024 Train AUC: 0.9703 Val AUC: 0.9587 Val PRC: 0.9571 Time: 0.23\n",
      "Epoch: 129 Train Loss: 0.1987 Acc: 0.9022 Pre: 0.9006 Recall: 0.9041 F1: 0.9023 Train AUC: 0.9759 Val AUC: 0.9593 Val PRC: 0.9525 Time: 0.23\n",
      "Epoch: 130 Train Loss: 0.2198 Acc: 0.9041 Pre: 0.8918 Recall: 0.9198 F1: 0.9056 Train AUC: 0.9711 Val AUC: 0.9642 Val PRC: 0.9593 Time: 0.24\n",
      "Epoch: 131 Train Loss: 0.2057 Acc: 0.9012 Pre: 0.8741 Recall: 0.9374 F1: 0.9046 Train AUC: 0.9746 Val AUC: 0.9602 Val PRC: 0.9573 Time: 0.23\n",
      "Epoch: 132 Train Loss: 0.2095 Acc: 0.8943 Pre: 0.8824 Recall: 0.9100 F1: 0.8960 Train AUC: 0.9741 Val AUC: 0.9617 Val PRC: 0.9568 Time: 0.23\n",
      "Epoch: 133 Train Loss: 0.2013 Acc: 0.8933 Pre: 0.8807 Recall: 0.9100 F1: 0.8951 Train AUC: 0.9753 Val AUC: 0.9599 Val PRC: 0.9555 Time: 0.24\n",
      "Epoch: 134 Train Loss: 0.2178 Acc: 0.8982 Pre: 0.8967 Recall: 0.9002 F1: 0.8984 Train AUC: 0.9704 Val AUC: 0.9621 Val PRC: 0.9614 Time: 0.23\n",
      "Epoch: 135 Train Loss: 0.2103 Acc: 0.9041 Pre: 0.8964 Recall: 0.9139 F1: 0.9050 Train AUC: 0.9732 Val AUC: 0.9635 Val PRC: 0.9605 Time: 0.24\n",
      "Epoch: 136 Train Loss: 0.2149 Acc: 0.8992 Pre: 0.9130 Recall: 0.8826 F1: 0.8975 Train AUC: 0.9718 Val AUC: 0.9615 Val PRC: 0.9601 Time: 0.24\n",
      "Epoch: 137 Train Loss: 0.2080 Acc: 0.9041 Pre: 0.9057 Recall: 0.9022 F1: 0.9039 Train AUC: 0.9734 Val AUC: 0.9586 Val PRC: 0.9577 Time: 0.23\n",
      "Epoch: 138 Train Loss: 0.2027 Acc: 0.9119 Pre: 0.8994 Recall: 0.9276 F1: 0.9133 Train AUC: 0.9749 Val AUC: 0.9634 Val PRC: 0.9620 Time: 0.23\n",
      "Epoch: 139 Train Loss: 0.2041 Acc: 0.8992 Pre: 0.8953 Recall: 0.9041 F1: 0.8997 Train AUC: 0.9743 Val AUC: 0.9623 Val PRC: 0.9566 Time: 0.23\n",
      "Epoch: 140 Train Loss: 0.2045 Acc: 0.8992 Pre: 0.8820 Recall: 0.9217 F1: 0.9014 Train AUC: 0.9747 Val AUC: 0.9617 Val PRC: 0.9565 Time: 0.23\n",
      "Epoch: 141 Train Loss: 0.2160 Acc: 0.8992 Pre: 0.8736 Recall: 0.9335 F1: 0.9026 Train AUC: 0.9718 Val AUC: 0.9636 Val PRC: 0.9587 Time: 0.23\n",
      "Epoch: 142 Train Loss: 0.2025 Acc: 0.9012 Pre: 0.8727 Recall: 0.9393 F1: 0.9048 Train AUC: 0.9749 Val AUC: 0.9631 Val PRC: 0.9567 Time: 0.23\n",
      "Epoch: 143 Train Loss: 0.2127 Acc: 0.8953 Pre: 0.8727 Recall: 0.9256 F1: 0.8984 Train AUC: 0.9717 Val AUC: 0.9609 Val PRC: 0.9597 Time: 0.23\n",
      "Epoch: 144 Train Loss: 0.1996 Acc: 0.9041 Pre: 0.8918 Recall: 0.9198 F1: 0.9056 Train AUC: 0.9760 Val AUC: 0.9644 Val PRC: 0.9638 Time: 0.23\n",
      "Epoch: 145 Train Loss: 0.2062 Acc: 0.9080 Pre: 0.8897 Recall: 0.9315 F1: 0.9101 Train AUC: 0.9740 Val AUC: 0.9618 Val PRC: 0.9557 Time: 0.23\n",
      "Epoch: 146 Train Loss: 0.2093 Acc: 0.9061 Pre: 0.8937 Recall: 0.9217 F1: 0.9075 Train AUC: 0.9729 Val AUC: 0.9610 Val PRC: 0.9550 Time: 0.24\n",
      "Epoch: 147 Train Loss: 0.1970 Acc: 0.9022 Pre: 0.8799 Recall: 0.9315 F1: 0.9049 Train AUC: 0.9761 Val AUC: 0.9636 Val PRC: 0.9576 Time: 0.24\n",
      "Epoch: 148 Train Loss: 0.2011 Acc: 0.9051 Pre: 0.8966 Recall: 0.9159 F1: 0.9061 Train AUC: 0.9763 Val AUC: 0.9646 Val PRC: 0.9604 Time: 0.23\n",
      "Epoch: 149 Train Loss: 0.2111 Acc: 0.9061 Pre: 0.8998 Recall: 0.9139 F1: 0.9068 Train AUC: 0.9731 Val AUC: 0.9626 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 150 Train Loss: 0.2028 Acc: 0.9031 Pre: 0.8815 Recall: 0.9315 F1: 0.9058 Train AUC: 0.9749 Val AUC: 0.9614 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 151 Train Loss: 0.1980 Acc: 0.9041 Pre: 0.8904 Recall: 0.9217 F1: 0.9058 Train AUC: 0.9761 Val AUC: 0.9606 Val PRC: 0.9602 Time: 0.24\n",
      "Epoch: 152 Train Loss: 0.2014 Acc: 0.9012 Pre: 0.8727 Recall: 0.9393 F1: 0.9048 Train AUC: 0.9752 Val AUC: 0.9638 Val PRC: 0.9625 Time: 0.24\n",
      "Epoch: 153 Train Loss: 0.2001 Acc: 0.9061 Pre: 0.9125 Recall: 0.8982 F1: 0.9053 Train AUC: 0.9762 Val AUC: 0.9640 Val PRC: 0.9649 Time: 0.24\n",
      "Epoch: 154 Train Loss: 0.2092 Acc: 0.9051 Pre: 0.8906 Recall: 0.9237 F1: 0.9068 Train AUC: 0.9732 Val AUC: 0.9652 Val PRC: 0.9658 Time: 0.24\n",
      "Epoch: 155 Train Loss: 0.1895 Acc: 0.9080 Pre: 0.8912 Recall: 0.9295 F1: 0.9100 Train AUC: 0.9784 Val AUC: 0.9655 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 156 Train Loss: 0.2033 Acc: 0.8992 Pre: 0.8696 Recall: 0.9393 F1: 0.9031 Train AUC: 0.9752 Val AUC: 0.9624 Val PRC: 0.9557 Time: 0.23\n",
      "Epoch: 157 Train Loss: 0.2075 Acc: 0.9051 Pre: 0.8833 Recall: 0.9335 F1: 0.9077 Train AUC: 0.9739 Val AUC: 0.9623 Val PRC: 0.9594 Time: 0.24\n",
      "Epoch: 158 Train Loss: 0.1963 Acc: 0.9051 Pre: 0.8906 Recall: 0.9237 F1: 0.9068 Train AUC: 0.9760 Val AUC: 0.9638 Val PRC: 0.9596 Time: 0.24\n",
      "Epoch: 159 Train Loss: 0.2178 Acc: 0.9022 Pre: 0.8944 Recall: 0.9119 F1: 0.9031 Train AUC: 0.9720 Val AUC: 0.9638 Val PRC: 0.9617 Time: 0.24\n",
      "Epoch: 160 Train Loss: 0.1932 Acc: 0.9012 Pre: 0.9051 Recall: 0.8963 F1: 0.9007 Train AUC: 0.9775 Val AUC: 0.9641 Val PRC: 0.9596 Time: 0.23\n",
      "Epoch: 161 Train Loss: 0.1833 Acc: 0.9031 Pre: 0.8992 Recall: 0.9080 F1: 0.9036 Train AUC: 0.9797 Val AUC: 0.9637 Val PRC: 0.9656 Time: 0.23\n",
      "Epoch: 162 Train Loss: 0.1826 Acc: 0.8973 Pre: 0.8859 Recall: 0.9119 F1: 0.8987 Train AUC: 0.9796 Val AUC: 0.9636 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 163 Train Loss: 0.2031 Acc: 0.9061 Pre: 0.9209 Recall: 0.8885 F1: 0.9044 Train AUC: 0.9744 Val AUC: 0.9626 Val PRC: 0.9650 Time: 0.23\n",
      "Epoch: 164 Train Loss: 0.1830 Acc: 0.9031 Pre: 0.8829 Recall: 0.9295 F1: 0.9056 Train AUC: 0.9797 Val AUC: 0.9657 Val PRC: 0.9600 Time: 0.24\n",
      "Epoch: 165 Train Loss: 0.1930 Acc: 0.9041 Pre: 0.9105 Recall: 0.8963 F1: 0.9034 Train AUC: 0.9774 Val AUC: 0.9665 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 166 Train Loss: 0.1955 Acc: 0.9061 Pre: 0.8835 Recall: 0.9354 F1: 0.9087 Train AUC: 0.9760 Val AUC: 0.9667 Val PRC: 0.9679 Time: 0.23\n",
      "Epoch: 167 Train Loss: 0.1855 Acc: 0.9012 Pre: 0.8782 Recall: 0.9315 F1: 0.9041 Train AUC: 0.9787 Val AUC: 0.9647 Val PRC: 0.9641 Time: 0.23\n",
      "Epoch: 168 Train Loss: 0.1927 Acc: 0.9031 Pre: 0.8902 Recall: 0.9198 F1: 0.9047 Train AUC: 0.9764 Val AUC: 0.9644 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 169 Train Loss: 0.1929 Acc: 0.9090 Pre: 0.8989 Recall: 0.9217 F1: 0.9101 Train AUC: 0.9761 Val AUC: 0.9655 Val PRC: 0.9649 Time: 0.23\n",
      "Epoch: 170 Train Loss: 0.1896 Acc: 0.9051 Pre: 0.9059 Recall: 0.9041 F1: 0.9050 Train AUC: 0.9773 Val AUC: 0.9672 Val PRC: 0.9656 Time: 0.23\n",
      "Epoch: 171 Train Loss: 0.1890 Acc: 0.9090 Pre: 0.9066 Recall: 0.9119 F1: 0.9093 Train AUC: 0.9779 Val AUC: 0.9642 Val PRC: 0.9620 Time: 0.23\n",
      "Epoch: 172 Train Loss: 0.1842 Acc: 0.9110 Pre: 0.9217 Recall: 0.8982 F1: 0.9098 Train AUC: 0.9787 Val AUC: 0.9643 Val PRC: 0.9591 Time: 0.23\n",
      "Epoch: 173 Train Loss: 0.1869 Acc: 0.9129 Pre: 0.9027 Recall: 0.9256 F1: 0.9140 Train AUC: 0.9782 Val AUC: 0.9655 Val PRC: 0.9623 Time: 0.24\n",
      "Epoch: 174 Train Loss: 0.2009 Acc: 0.9070 Pre: 0.9000 Recall: 0.9159 F1: 0.9079 Train AUC: 0.9749 Val AUC: 0.9636 Val PRC: 0.9632 Time: 0.24\n",
      "Epoch: 175 Train Loss: 0.1837 Acc: 0.9100 Pre: 0.8945 Recall: 0.9295 F1: 0.9117 Train AUC: 0.9789 Val AUC: 0.9648 Val PRC: 0.9632 Time: 0.24\n",
      "Epoch: 176 Train Loss: 0.1810 Acc: 0.9159 Pre: 0.9017 Recall: 0.9335 F1: 0.9173 Train AUC: 0.9794 Val AUC: 0.9684 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 177 Train Loss: 0.1920 Acc: 0.9022 Pre: 0.8929 Recall: 0.9139 F1: 0.9033 Train AUC: 0.9761 Val AUC: 0.9605 Val PRC: 0.9595 Time: 0.24\n",
      "Epoch: 178 Train Loss: 0.1963 Acc: 0.9129 Pre: 0.9058 Recall: 0.9217 F1: 0.9137 Train AUC: 0.9746 Val AUC: 0.9652 Val PRC: 0.9646 Time: 0.23\n",
      "Epoch: 179 Train Loss: 0.1777 Acc: 0.9051 Pre: 0.8935 Recall: 0.9198 F1: 0.9065 Train AUC: 0.9801 Val AUC: 0.9662 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 180 Train Loss: 0.1913 Acc: 0.9139 Pre: 0.9139 Recall: 0.9139 F1: 0.9139 Train AUC: 0.9768 Val AUC: 0.9676 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 181 Train Loss: 0.1932 Acc: 0.9129 Pre: 0.9089 Recall: 0.9178 F1: 0.9133 Train AUC: 0.9763 Val AUC: 0.9674 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 182 Train Loss: 0.1993 Acc: 0.9129 Pre: 0.9203 Recall: 0.9041 F1: 0.9121 Train AUC: 0.9754 Val AUC: 0.9677 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 183 Train Loss: 0.1758 Acc: 0.9119 Pre: 0.9040 Recall: 0.9217 F1: 0.9128 Train AUC: 0.9806 Val AUC: 0.9660 Val PRC: 0.9683 Time: 0.24\n",
      "Epoch: 184 Train Loss: 0.1804 Acc: 0.9051 Pre: 0.8805 Recall: 0.9374 F1: 0.9081 Train AUC: 0.9805 Val AUC: 0.9642 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 185 Train Loss: 0.1748 Acc: 0.9061 Pre: 0.8780 Recall: 0.9432 F1: 0.9094 Train AUC: 0.9808 Val AUC: 0.9635 Val PRC: 0.9615 Time: 0.23\n",
      "Epoch: 186 Train Loss: 0.1657 Acc: 0.9051 Pre: 0.8876 Recall: 0.9276 F1: 0.9072 Train AUC: 0.9829 Val AUC: 0.9662 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 187 Train Loss: 0.1716 Acc: 0.9090 Pre: 0.9098 Recall: 0.9080 F1: 0.9089 Train AUC: 0.9816 Val AUC: 0.9681 Val PRC: 0.9630 Time: 0.23\n",
      "Epoch: 188 Train Loss: 0.1823 Acc: 0.9070 Pre: 0.9015 Recall: 0.9139 F1: 0.9077 Train AUC: 0.9788 Val AUC: 0.9659 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 189 Train Loss: 0.1773 Acc: 0.9080 Pre: 0.9080 Recall: 0.9080 F1: 0.9080 Train AUC: 0.9804 Val AUC: 0.9682 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 190 Train Loss: 0.1714 Acc: 0.9119 Pre: 0.9305 Recall: 0.8904 F1: 0.9100 Train AUC: 0.9812 Val AUC: 0.9680 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 191 Train Loss: 0.1666 Acc: 0.9100 Pre: 0.8975 Recall: 0.9256 F1: 0.9114 Train AUC: 0.9826 Val AUC: 0.9669 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 192 Train Loss: 0.1787 Acc: 0.9070 Pre: 0.9127 Recall: 0.9002 F1: 0.9064 Train AUC: 0.9799 Val AUC: 0.9654 Val PRC: 0.9655 Time: 0.23\n",
      "Epoch: 193 Train Loss: 0.1845 Acc: 0.9119 Pre: 0.9056 Recall: 0.9198 F1: 0.9126 Train AUC: 0.9787 Val AUC: 0.9658 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 194 Train Loss: 0.1683 Acc: 0.9119 Pre: 0.8920 Recall: 0.9374 F1: 0.9141 Train AUC: 0.9817 Val AUC: 0.9668 Val PRC: 0.9607 Time: 0.24\n",
      "Epoch: 195 Train Loss: 0.1833 Acc: 0.9080 Pre: 0.8840 Recall: 0.9393 F1: 0.9108 Train AUC: 0.9775 Val AUC: 0.9628 Val PRC: 0.9631 Time: 0.24\n",
      "Epoch: 196 Train Loss: 0.1780 Acc: 0.9129 Pre: 0.9042 Recall: 0.9237 F1: 0.9138 Train AUC: 0.9799 Val AUC: 0.9658 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 197 Train Loss: 0.1788 Acc: 0.9119 Pre: 0.8877 Recall: 0.9432 F1: 0.9146 Train AUC: 0.9797 Val AUC: 0.9671 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 198 Train Loss: 0.1746 Acc: 0.9110 Pre: 0.9150 Recall: 0.9061 F1: 0.9105 Train AUC: 0.9808 Val AUC: 0.9656 Val PRC: 0.9653 Time: 0.23\n",
      "Epoch: 199 Train Loss: 0.1820 Acc: 0.9100 Pre: 0.8975 Recall: 0.9256 F1: 0.9114 Train AUC: 0.9790 Val AUC: 0.9626 Val PRC: 0.9580 Time: 0.23\n",
      "Epoch: 200 Train Loss: 0.1749 Acc: 0.8973 Pre: 0.8664 Recall: 0.9393 F1: 0.9014 Train AUC: 0.9806 Val AUC: 0.9662 Val PRC: 0.9671 Time: 0.23\n",
      "Epoch: 201 Train Loss: 0.1764 Acc: 0.9149 Pre: 0.9109 Recall: 0.9198 F1: 0.9153 Train AUC: 0.9812 Val AUC: 0.9669 Val PRC: 0.9652 Time: 0.23\n",
      "Epoch: 202 Train Loss: 0.1619 Acc: 0.9159 Pre: 0.9110 Recall: 0.9217 F1: 0.9163 Train AUC: 0.9832 Val AUC: 0.9674 Val PRC: 0.9618 Time: 0.24\n",
      "Epoch: 203 Train Loss: 0.1786 Acc: 0.9031 Pre: 0.8745 Recall: 0.9413 F1: 0.9067 Train AUC: 0.9802 Val AUC: 0.9638 Val PRC: 0.9575 Time: 0.23\n",
      "Epoch: 204 Train Loss: 0.1793 Acc: 0.9051 Pre: 0.8736 Recall: 0.9472 F1: 0.9089 Train AUC: 0.9785 Val AUC: 0.9663 Val PRC: 0.9624 Time: 0.23\n",
      "Epoch: 205 Train Loss: 0.1794 Acc: 0.9061 Pre: 0.8879 Recall: 0.9295 F1: 0.9082 Train AUC: 0.9804 Val AUC: 0.9679 Val PRC: 0.9653 Time: 0.23\n",
      "Epoch: 206 Train Loss: 0.1583 Acc: 0.9149 Pre: 0.9240 Recall: 0.9041 F1: 0.9139 Train AUC: 0.9843 Val AUC: 0.9692 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 207 Train Loss: 0.1702 Acc: 0.9110 Pre: 0.9054 Recall: 0.9178 F1: 0.9116 Train AUC: 0.9810 Val AUC: 0.9681 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 208 Train Loss: 0.1705 Acc: 0.9129 Pre: 0.9105 Recall: 0.9159 F1: 0.9132 Train AUC: 0.9817 Val AUC: 0.9676 Val PRC: 0.9656 Time: 0.23\n",
      "Epoch: 209 Train Loss: 0.1823 Acc: 0.9119 Pre: 0.9270 Recall: 0.8943 F1: 0.9104 Train AUC: 0.9784 Val AUC: 0.9680 Val PRC: 0.9695 Time: 0.23\n",
      "Epoch: 210 Train Loss: 0.1689 Acc: 0.9070 Pre: 0.8925 Recall: 0.9256 F1: 0.9087 Train AUC: 0.9824 Val AUC: 0.9665 Val PRC: 0.9676 Time: 0.23\n",
      "Epoch: 211 Train Loss: 0.1618 Acc: 0.9080 Pre: 0.9017 Recall: 0.9159 F1: 0.9087 Train AUC: 0.9825 Val AUC: 0.9682 Val PRC: 0.9683 Time: 0.23\n",
      "Epoch: 212 Train Loss: 0.1695 Acc: 0.9041 Pre: 0.8831 Recall: 0.9315 F1: 0.9067 Train AUC: 0.9814 Val AUC: 0.9607 Val PRC: 0.9562 Time: 0.24\n",
      "Epoch: 213 Train Loss: 0.1733 Acc: 0.9090 Pre: 0.9066 Recall: 0.9119 F1: 0.9093 Train AUC: 0.9793 Val AUC: 0.9639 Val PRC: 0.9613 Time: 0.23\n",
      "Epoch: 214 Train Loss: 0.1790 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9806 Val AUC: 0.9685 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 215 Train Loss: 0.1670 Acc: 0.9129 Pre: 0.9121 Recall: 0.9139 F1: 0.9130 Train AUC: 0.9822 Val AUC: 0.9687 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 216 Train Loss: 0.1647 Acc: 0.9110 Pre: 0.9102 Recall: 0.9119 F1: 0.9110 Train AUC: 0.9828 Val AUC: 0.9681 Val PRC: 0.9695 Time: 0.23\n",
      "Epoch: 217 Train Loss: 0.1585 Acc: 0.9041 Pre: 0.8904 Recall: 0.9217 F1: 0.9058 Train AUC: 0.9861 Val AUC: 0.9631 Val PRC: 0.9622 Time: 0.23\n",
      "Epoch: 218 Train Loss: 0.1876 Acc: 0.9051 Pre: 0.8805 Recall: 0.9374 F1: 0.9081 Train AUC: 0.9768 Val AUC: 0.9649 Val PRC: 0.9656 Time: 0.42\n",
      "Epoch: 219 Train Loss: 0.1654 Acc: 0.9100 Pre: 0.8945 Recall: 0.9295 F1: 0.9117 Train AUC: 0.9849 Val AUC: 0.9691 Val PRC: 0.9708 Time: 0.24\n",
      "Epoch: 220 Train Loss: 0.1585 Acc: 0.9080 Pre: 0.8897 Recall: 0.9315 F1: 0.9101 Train AUC: 0.9848 Val AUC: 0.9682 Val PRC: 0.9690 Time: 0.25\n",
      "Epoch: 221 Train Loss: 0.1683 Acc: 0.9022 Pre: 0.8856 Recall: 0.9237 F1: 0.9042 Train AUC: 0.9813 Val AUC: 0.9667 Val PRC: 0.9662 Time: 0.24\n",
      "Epoch: 222 Train Loss: 0.1596 Acc: 0.9051 Pre: 0.8848 Recall: 0.9315 F1: 0.9075 Train AUC: 0.9844 Val AUC: 0.9663 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 223 Train Loss: 0.1828 Acc: 0.9061 Pre: 0.8879 Recall: 0.9295 F1: 0.9082 Train AUC: 0.9776 Val AUC: 0.9642 Val PRC: 0.9588 Time: 0.23\n",
      "Epoch: 224 Train Loss: 0.1714 Acc: 0.9139 Pre: 0.9222 Recall: 0.9041 F1: 0.9130 Train AUC: 0.9812 Val AUC: 0.9690 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 225 Train Loss: 0.1669 Acc: 0.9139 Pre: 0.9139 Recall: 0.9139 F1: 0.9139 Train AUC: 0.9844 Val AUC: 0.9658 Val PRC: 0.9679 Time: 0.23\n",
      "Epoch: 226 Train Loss: 0.1600 Acc: 0.9178 Pre: 0.9194 Recall: 0.9159 F1: 0.9176 Train AUC: 0.9835 Val AUC: 0.9700 Val PRC: 0.9722 Time: 0.23\n",
      "Epoch: 227 Train Loss: 0.1622 Acc: 0.8992 Pre: 0.8736 Recall: 0.9335 F1: 0.9026 Train AUC: 0.9836 Val AUC: 0.9626 Val PRC: 0.9613 Time: 0.23\n",
      "Epoch: 228 Train Loss: 0.1592 Acc: 0.9051 Pre: 0.8981 Recall: 0.9139 F1: 0.9059 Train AUC: 0.9840 Val AUC: 0.9648 Val PRC: 0.9645 Time: 0.23\n",
      "Epoch: 229 Train Loss: 0.1761 Acc: 0.9139 Pre: 0.8983 Recall: 0.9335 F1: 0.9155 Train AUC: 0.9806 Val AUC: 0.9669 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 230 Train Loss: 0.1767 Acc: 0.9100 Pre: 0.9037 Recall: 0.9178 F1: 0.9107 Train AUC: 0.9804 Val AUC: 0.9671 Val PRC: 0.9694 Time: 0.24\n",
      "Epoch: 231 Train Loss: 0.1672 Acc: 0.9168 Pre: 0.9347 Recall: 0.8963 F1: 0.9151 Train AUC: 0.9823 Val AUC: 0.9708 Val PRC: 0.9726 Time: 0.23\n",
      "Epoch: 232 Train Loss: 0.1598 Acc: 0.9149 Pre: 0.9257 Recall: 0.9022 F1: 0.9138 Train AUC: 0.9829 Val AUC: 0.9710 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 233 Train Loss: 0.1637 Acc: 0.9139 Pre: 0.9308 Recall: 0.8943 F1: 0.9122 Train AUC: 0.9828 Val AUC: 0.9693 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 234 Train Loss: 0.1597 Acc: 0.9198 Pre: 0.9264 Recall: 0.9119 F1: 0.9191 Train AUC: 0.9830 Val AUC: 0.9699 Val PRC: 0.9699 Time: 0.23\n",
      "Epoch: 235 Train Loss: 0.1519 Acc: 0.9139 Pre: 0.9060 Recall: 0.9237 F1: 0.9147 Train AUC: 0.9847 Val AUC: 0.9692 Val PRC: 0.9696 Time: 0.24\n",
      "Epoch: 236 Train Loss: 0.1522 Acc: 0.9139 Pre: 0.9188 Recall: 0.9080 F1: 0.9134 Train AUC: 0.9857 Val AUC: 0.9680 Val PRC: 0.9692 Time: 0.25\n",
      "Epoch: 237 Train Loss: 0.1809 Acc: 0.9159 Pre: 0.9310 Recall: 0.8982 F1: 0.9143 Train AUC: 0.9835 Val AUC: 0.9718 Val PRC: 0.9741 Time: 0.25\n",
      "Epoch: 238 Train Loss: 0.1525 Acc: 0.9129 Pre: 0.8850 Recall: 0.9491 F1: 0.9160 Train AUC: 0.9844 Val AUC: 0.9701 Val PRC: 0.9703 Time: 0.24\n",
      "Epoch: 239 Train Loss: 0.1565 Acc: 0.9100 Pre: 0.8844 Recall: 0.9432 F1: 0.9129 Train AUC: 0.9835 Val AUC: 0.9687 Val PRC: 0.9702 Time: 0.24\n",
      "Epoch: 240 Train Loss: 0.1645 Acc: 0.9080 Pre: 0.8826 Recall: 0.9413 F1: 0.9110 Train AUC: 0.9819 Val AUC: 0.9692 Val PRC: 0.9701 Time: 0.26\n",
      "Epoch: 241 Train Loss: 0.1607 Acc: 0.9168 Pre: 0.9329 Recall: 0.8982 F1: 0.9153 Train AUC: 0.9830 Val AUC: 0.9695 Val PRC: 0.9719 Time: 0.23\n",
      "Epoch: 242 Train Loss: 0.1491 Acc: 0.9276 Pre: 0.9344 Recall: 0.9198 F1: 0.9270 Train AUC: 0.9854 Val AUC: 0.9700 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 243 Train Loss: 0.1692 Acc: 0.9178 Pre: 0.9261 Recall: 0.9080 F1: 0.9170 Train AUC: 0.9809 Val AUC: 0.9689 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 244 Train Loss: 0.1407 Acc: 0.9139 Pre: 0.9172 Recall: 0.9100 F1: 0.9136 Train AUC: 0.9863 Val AUC: 0.9721 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 245 Train Loss: 0.1567 Acc: 0.9178 Pre: 0.9228 Recall: 0.9119 F1: 0.9173 Train AUC: 0.9822 Val AUC: 0.9680 Val PRC: 0.9694 Time: 0.23\n",
      "Epoch: 246 Train Loss: 0.1579 Acc: 0.9168 Pre: 0.9419 Recall: 0.8885 F1: 0.9144 Train AUC: 0.9833 Val AUC: 0.9681 Val PRC: 0.9693 Time: 0.23\n",
      "Epoch: 247 Train Loss: 0.1379 Acc: 0.9090 Pre: 0.8870 Recall: 0.9374 F1: 0.9115 Train AUC: 0.9877 Val AUC: 0.9682 Val PRC: 0.9679 Time: 0.23\n",
      "Epoch: 248 Train Loss: 0.1488 Acc: 0.9178 Pre: 0.9348 Recall: 0.8982 F1: 0.9162 Train AUC: 0.9834 Val AUC: 0.9682 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 249 Train Loss: 0.1390 Acc: 0.9100 Pre: 0.9006 Recall: 0.9217 F1: 0.9110 Train AUC: 0.9871 Val AUC: 0.9701 Val PRC: 0.9714 Time: 0.24\n",
      "Epoch: 250 Train Loss: 0.1421 Acc: 0.9119 Pre: 0.9152 Recall: 0.9080 F1: 0.9116 Train AUC: 0.9861 Val AUC: 0.9696 Val PRC: 0.9714 Time: 0.23\n",
      "Epoch: 251 Train Loss: 0.1431 Acc: 0.9188 Pre: 0.9263 Recall: 0.9100 F1: 0.9181 Train AUC: 0.9866 Val AUC: 0.9688 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 252 Train Loss: 0.1387 Acc: 0.9149 Pre: 0.9141 Recall: 0.9159 F1: 0.9150 Train AUC: 0.9865 Val AUC: 0.9702 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 253 Train Loss: 0.1425 Acc: 0.9188 Pre: 0.9246 Recall: 0.9119 F1: 0.9182 Train AUC: 0.9865 Val AUC: 0.9709 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 254 Train Loss: 0.1317 Acc: 0.9168 Pre: 0.9329 Recall: 0.8982 F1: 0.9153 Train AUC: 0.9888 Val AUC: 0.9681 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 255 Train Loss: 0.1411 Acc: 0.9227 Pre: 0.9202 Recall: 0.9256 F1: 0.9229 Train AUC: 0.9859 Val AUC: 0.9709 Val PRC: 0.9715 Time: 0.23\n",
      "Epoch: 256 Train Loss: 0.1404 Acc: 0.9188 Pre: 0.9115 Recall: 0.9276 F1: 0.9195 Train AUC: 0.9855 Val AUC: 0.9685 Val PRC: 0.9675 Time: 0.23\n",
      "Epoch: 257 Train Loss: 0.1342 Acc: 0.9168 Pre: 0.9383 Recall: 0.8924 F1: 0.9147 Train AUC: 0.9876 Val AUC: 0.9697 Val PRC: 0.9714 Time: 0.24\n",
      "Epoch: 258 Train Loss: 0.1385 Acc: 0.9198 Pre: 0.9198 Recall: 0.9198 F1: 0.9198 Train AUC: 0.9868 Val AUC: 0.9701 Val PRC: 0.9719 Time: 0.24\n",
      "Epoch: 259 Train Loss: 0.1366 Acc: 0.9207 Pre: 0.9517 Recall: 0.8865 F1: 0.9179 Train AUC: 0.9869 Val AUC: 0.9693 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 260 Train Loss: 0.1413 Acc: 0.9178 Pre: 0.9130 Recall: 0.9237 F1: 0.9183 Train AUC: 0.9861 Val AUC: 0.9701 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 261 Train Loss: 0.1414 Acc: 0.9198 Pre: 0.9369 Recall: 0.9002 F1: 0.9182 Train AUC: 0.9856 Val AUC: 0.9695 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 262 Train Loss: 0.1599 Acc: 0.9237 Pre: 0.9339 Recall: 0.9119 F1: 0.9228 Train AUC: 0.9870 Val AUC: 0.9705 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 263 Train Loss: 0.1396 Acc: 0.9227 Pre: 0.9372 Recall: 0.9061 F1: 0.9214 Train AUC: 0.9868 Val AUC: 0.9693 Val PRC: 0.9715 Time: 0.23\n",
      "Epoch: 264 Train Loss: 0.1272 Acc: 0.9110 Pre: 0.9086 Recall: 0.9139 F1: 0.9112 Train AUC: 0.9884 Val AUC: 0.9694 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 265 Train Loss: 0.1503 Acc: 0.9100 Pre: 0.9182 Recall: 0.9002 F1: 0.9091 Train AUC: 0.9846 Val AUC: 0.9663 Val PRC: 0.9671 Time: 0.23\n",
      "Epoch: 266 Train Loss: 0.1332 Acc: 0.9168 Pre: 0.9176 Recall: 0.9159 F1: 0.9167 Train AUC: 0.9875 Val AUC: 0.9701 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 267 Train Loss: 0.1364 Acc: 0.9168 Pre: 0.9176 Recall: 0.9159 F1: 0.9167 Train AUC: 0.9874 Val AUC: 0.9682 Val PRC: 0.9710 Time: 0.23\n",
      "Epoch: 268 Train Loss: 0.1480 Acc: 0.9256 Pre: 0.9466 Recall: 0.9022 F1: 0.9238 Train AUC: 0.9849 Val AUC: 0.9713 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 269 Train Loss: 0.1345 Acc: 0.9188 Pre: 0.9115 Recall: 0.9276 F1: 0.9195 Train AUC: 0.9872 Val AUC: 0.9699 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 270 Train Loss: 0.1464 Acc: 0.9207 Pre: 0.9479 Recall: 0.8904 F1: 0.9183 Train AUC: 0.9852 Val AUC: 0.9704 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 271 Train Loss: 0.1285 Acc: 0.9149 Pre: 0.9257 Recall: 0.9022 F1: 0.9138 Train AUC: 0.9882 Val AUC: 0.9705 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 272 Train Loss: 0.1366 Acc: 0.9217 Pre: 0.9425 Recall: 0.8982 F1: 0.9198 Train AUC: 0.9892 Val AUC: 0.9683 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 273 Train Loss: 0.1403 Acc: 0.9188 Pre: 0.9367 Recall: 0.8982 F1: 0.9171 Train AUC: 0.9859 Val AUC: 0.9712 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 274 Train Loss: 0.1234 Acc: 0.9188 Pre: 0.9612 Recall: 0.8728 F1: 0.9149 Train AUC: 0.9892 Val AUC: 0.9702 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 275 Train Loss: 0.1208 Acc: 0.9217 Pre: 0.9371 Recall: 0.9041 F1: 0.9203 Train AUC: 0.9895 Val AUC: 0.9665 Val PRC: 0.9668 Time: 0.23\n",
      "Epoch: 276 Train Loss: 0.1301 Acc: 0.9149 Pre: 0.9240 Recall: 0.9041 F1: 0.9139 Train AUC: 0.9882 Val AUC: 0.9690 Val PRC: 0.9716 Time: 0.23\n",
      "Epoch: 277 Train Loss: 0.1383 Acc: 0.9178 Pre: 0.9261 Recall: 0.9080 F1: 0.9170 Train AUC: 0.9866 Val AUC: 0.9697 Val PRC: 0.9710 Time: 0.24\n",
      "Epoch: 278 Train Loss: 0.1309 Acc: 0.9139 Pre: 0.9397 Recall: 0.8845 F1: 0.9113 Train AUC: 0.9878 Val AUC: 0.9716 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 279 Train Loss: 0.1340 Acc: 0.9188 Pre: 0.9403 Recall: 0.8943 F1: 0.9168 Train AUC: 0.9868 Val AUC: 0.9679 Val PRC: 0.9699 Time: 0.23\n",
      "Epoch: 280 Train Loss: 0.1496 Acc: 0.9159 Pre: 0.9276 Recall: 0.9022 F1: 0.9147 Train AUC: 0.9836 Val AUC: 0.9695 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 281 Train Loss: 0.1351 Acc: 0.9159 Pre: 0.9208 Recall: 0.9100 F1: 0.9154 Train AUC: 0.9876 Val AUC: 0.9683 Val PRC: 0.9709 Time: 0.23\n",
      "Epoch: 282 Train Loss: 0.1317 Acc: 0.9207 Pre: 0.9442 Recall: 0.8943 F1: 0.9186 Train AUC: 0.9882 Val AUC: 0.9675 Val PRC: 0.9697 Time: 0.23\n",
      "Epoch: 283 Train Loss: 0.1309 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9870 Val AUC: 0.9676 Val PRC: 0.9666 Time: 0.23\n",
      "Epoch: 284 Train Loss: 0.1408 Acc: 0.9119 Pre: 0.9103 Recall: 0.9139 F1: 0.9121 Train AUC: 0.9861 Val AUC: 0.9646 Val PRC: 0.9584 Time: 0.23\n",
      "Epoch: 285 Train Loss: 0.1318 Acc: 0.9188 Pre: 0.9315 Recall: 0.9041 F1: 0.9176 Train AUC: 0.9869 Val AUC: 0.9658 Val PRC: 0.9640 Time: 0.23\n",
      "Epoch: 286 Train Loss: 0.1409 Acc: 0.9188 Pre: 0.9350 Recall: 0.9002 F1: 0.9172 Train AUC: 0.9865 Val AUC: 0.9674 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 287 Train Loss: 0.1350 Acc: 0.9198 Pre: 0.9351 Recall: 0.9022 F1: 0.9183 Train AUC: 0.9852 Val AUC: 0.9703 Val PRC: 0.9717 Time: 0.24\n",
      "Epoch: 288 Train Loss: 0.1500 Acc: 0.9276 Pre: 0.9487 Recall: 0.9041 F1: 0.9259 Train AUC: 0.9832 Val AUC: 0.9694 Val PRC: 0.9661 Time: 0.23\n",
      "Epoch: 289 Train Loss: 0.1262 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9884 Val AUC: 0.9712 Val PRC: 0.9719 Time: 0.23\n",
      "Epoch: 290 Train Loss: 0.1429 Acc: 0.9256 Pre: 0.9522 Recall: 0.8963 F1: 0.9234 Train AUC: 0.9854 Val AUC: 0.9708 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 291 Train Loss: 0.1268 Acc: 0.9168 Pre: 0.9401 Recall: 0.8904 F1: 0.9146 Train AUC: 0.9880 Val AUC: 0.9704 Val PRC: 0.9717 Time: 0.23\n",
      "Epoch: 292 Train Loss: 0.1241 Acc: 0.9178 Pre: 0.9279 Recall: 0.9061 F1: 0.9168 Train AUC: 0.9891 Val AUC: 0.9662 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 293 Train Loss: 0.1239 Acc: 0.9168 Pre: 0.9401 Recall: 0.8904 F1: 0.9146 Train AUC: 0.9890 Val AUC: 0.9685 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 294 Train Loss: 0.1311 Acc: 0.9159 Pre: 0.9363 Recall: 0.8924 F1: 0.9138 Train AUC: 0.9877 Val AUC: 0.9671 Val PRC: 0.9686 Time: 0.23\n",
      "Epoch: 295 Train Loss: 0.1249 Acc: 0.9217 Pre: 0.9284 Recall: 0.9139 F1: 0.9211 Train AUC: 0.9884 Val AUC: 0.9684 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 296 Train Loss: 0.1443 Acc: 0.9198 Pre: 0.9387 Recall: 0.8982 F1: 0.9180 Train AUC: 0.9849 Val AUC: 0.9693 Val PRC: 0.9719 Time: 0.24\n",
      "Epoch: 297 Train Loss: 0.1295 Acc: 0.9207 Pre: 0.9352 Recall: 0.9041 F1: 0.9194 Train AUC: 0.9877 Val AUC: 0.9690 Val PRC: 0.9717 Time: 0.23\n",
      "Epoch: 298 Train Loss: 0.1422 Acc: 0.9207 Pre: 0.9317 Recall: 0.9080 F1: 0.9197 Train AUC: 0.9854 Val AUC: 0.9686 Val PRC: 0.9707 Time: 0.23\n",
      "Epoch: 299 Train Loss: 0.1304 Acc: 0.9100 Pre: 0.8990 Recall: 0.9237 F1: 0.9112 Train AUC: 0.9876 Val AUC: 0.9684 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 300 Train Loss: 0.1229 Acc: 0.9178 Pre: 0.9130 Recall: 0.9237 F1: 0.9183 Train AUC: 0.9887 Val AUC: 0.9692 Val PRC: 0.9698 Time: 0.24\n",
      "Epoch: 301 Train Loss: 0.1145 Acc: 0.9188 Pre: 0.9196 Recall: 0.9178 F1: 0.9187 Train AUC: 0.9897 Val AUC: 0.9693 Val PRC: 0.9709 Time: 0.24\n",
      "Epoch: 302 Train Loss: 0.1181 Acc: 0.9207 Pre: 0.9317 Recall: 0.9080 F1: 0.9197 Train AUC: 0.9892 Val AUC: 0.9703 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 303 Train Loss: 0.1282 Acc: 0.9198 Pre: 0.9181 Recall: 0.9217 F1: 0.9199 Train AUC: 0.9878 Val AUC: 0.9688 Val PRC: 0.9722 Time: 0.23\n",
      "Epoch: 304 Train Loss: 0.1294 Acc: 0.9247 Pre: 0.9483 Recall: 0.8982 F1: 0.9226 Train AUC: 0.9873 Val AUC: 0.9707 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 305 Train Loss: 0.1228 Acc: 0.9266 Pre: 0.9431 Recall: 0.9080 F1: 0.9252 Train AUC: 0.9891 Val AUC: 0.9659 Val PRC: 0.9678 Time: 0.24\n",
      "Epoch: 306 Train Loss: 0.1260 Acc: 0.9227 Pre: 0.9372 Recall: 0.9061 F1: 0.9214 Train AUC: 0.9870 Val AUC: 0.9696 Val PRC: 0.9720 Time: 0.26\n",
      "Epoch: 307 Train Loss: 0.1297 Acc: 0.9178 Pre: 0.9331 Recall: 0.9002 F1: 0.9163 Train AUC: 0.9879 Val AUC: 0.9698 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 308 Train Loss: 0.1257 Acc: 0.9217 Pre: 0.9371 Recall: 0.9041 F1: 0.9203 Train AUC: 0.9875 Val AUC: 0.9696 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 309 Train Loss: 0.1305 Acc: 0.9237 Pre: 0.9636 Recall: 0.8806 F1: 0.9202 Train AUC: 0.9867 Val AUC: 0.9708 Val PRC: 0.9726 Time: 0.24\n",
      "Epoch: 310 Train Loss: 0.1271 Acc: 0.9256 Pre: 0.9503 Recall: 0.8982 F1: 0.9235 Train AUC: 0.9875 Val AUC: 0.9718 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 311 Train Loss: 0.1291 Acc: 0.9266 Pre: 0.9486 Recall: 0.9022 F1: 0.9248 Train AUC: 0.9869 Val AUC: 0.9707 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 312 Train Loss: 0.1343 Acc: 0.9247 Pre: 0.9597 Recall: 0.8865 F1: 0.9217 Train AUC: 0.9862 Val AUC: 0.9696 Val PRC: 0.9714 Time: 0.24\n",
      "Epoch: 313 Train Loss: 0.1258 Acc: 0.9247 Pre: 0.9483 Recall: 0.8982 F1: 0.9226 Train AUC: 0.9862 Val AUC: 0.9717 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 314 Train Loss: 0.1348 Acc: 0.9198 Pre: 0.9181 Recall: 0.9217 F1: 0.9199 Train AUC: 0.9870 Val AUC: 0.9698 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 315 Train Loss: 0.1225 Acc: 0.9266 Pre: 0.9467 Recall: 0.9041 F1: 0.9249 Train AUC: 0.9860 Val AUC: 0.9677 Val PRC: 0.9590 Time: 0.24\n",
      "Epoch: 316 Train Loss: 0.1331 Acc: 0.9178 Pre: 0.9439 Recall: 0.8885 F1: 0.9153 Train AUC: 0.9855 Val AUC: 0.9685 Val PRC: 0.9675 Time: 0.24\n",
      "Epoch: 317 Train Loss: 0.1170 Acc: 0.9237 Pre: 0.9287 Recall: 0.9178 F1: 0.9232 Train AUC: 0.9887 Val AUC: 0.9690 Val PRC: 0.9663 Time: 0.24\n",
      "Epoch: 318 Train Loss: 0.1344 Acc: 0.9178 Pre: 0.9348 Recall: 0.8982 F1: 0.9162 Train AUC: 0.9870 Val AUC: 0.9671 Val PRC: 0.9692 Time: 0.24\n",
      "Epoch: 319 Train Loss: 0.1236 Acc: 0.9276 Pre: 0.9344 Recall: 0.9198 F1: 0.9270 Train AUC: 0.9878 Val AUC: 0.9690 Val PRC: 0.9720 Time: 0.24\n",
      "Epoch: 320 Train Loss: 0.1172 Acc: 0.9237 Pre: 0.9374 Recall: 0.9080 F1: 0.9225 Train AUC: 0.9898 Val AUC: 0.9676 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 321 Train Loss: 0.1130 Acc: 0.9247 Pre: 0.9357 Recall: 0.9119 F1: 0.9237 Train AUC: 0.9901 Val AUC: 0.9695 Val PRC: 0.9725 Time: 0.25\n",
      "Epoch: 322 Train Loss: 0.1264 Acc: 0.9237 Pre: 0.9482 Recall: 0.8963 F1: 0.9215 Train AUC: 0.9874 Val AUC: 0.9691 Val PRC: 0.9719 Time: 0.25\n",
      "Epoch: 323 Train Loss: 0.1307 Acc: 0.9237 Pre: 0.9321 Recall: 0.9139 F1: 0.9229 Train AUC: 0.9869 Val AUC: 0.9664 Val PRC: 0.9677 Time: 0.24\n",
      "Epoch: 324 Train Loss: 0.1227 Acc: 0.9237 Pre: 0.9270 Recall: 0.9198 F1: 0.9234 Train AUC: 0.9888 Val AUC: 0.9717 Val PRC: 0.9728 Time: 0.25\n",
      "Epoch: 325 Train Loss: 0.1465 Acc: 0.9207 Pre: 0.9406 Recall: 0.8982 F1: 0.9189 Train AUC: 0.9883 Val AUC: 0.9702 Val PRC: 0.9722 Time: 0.25\n",
      "Epoch: 326 Train Loss: 0.1268 Acc: 0.9247 Pre: 0.9465 Recall: 0.9002 F1: 0.9228 Train AUC: 0.9870 Val AUC: 0.9707 Val PRC: 0.9728 Time: 0.25\n",
      "Epoch: 327 Train Loss: 0.1281 Acc: 0.9237 Pre: 0.9539 Recall: 0.8904 F1: 0.9211 Train AUC: 0.9879 Val AUC: 0.9696 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 328 Train Loss: 0.1249 Acc: 0.9286 Pre: 0.9433 Recall: 0.9119 F1: 0.9274 Train AUC: 0.9884 Val AUC: 0.9736 Val PRC: 0.9772 Time: 0.24\n",
      "Epoch: 329 Train Loss: 0.1180 Acc: 0.9325 Pre: 0.9510 Recall: 0.9119 F1: 0.9311 Train AUC: 0.9892 Val AUC: 0.9725 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 330 Train Loss: 0.1206 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9878 Val AUC: 0.9698 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 331 Train Loss: 0.1174 Acc: 0.9276 Pre: 0.9414 Recall: 0.9119 F1: 0.9264 Train AUC: 0.9895 Val AUC: 0.9709 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 332 Train Loss: 0.1369 Acc: 0.9227 Pre: 0.9320 Recall: 0.9119 F1: 0.9219 Train AUC: 0.9853 Val AUC: 0.9689 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 333 Train Loss: 0.1220 Acc: 0.9256 Pre: 0.9307 Recall: 0.9198 F1: 0.9252 Train AUC: 0.9877 Val AUC: 0.9695 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 334 Train Loss: 0.1161 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9892 Val AUC: 0.9679 Val PRC: 0.9678 Time: 0.24\n",
      "Epoch: 335 Train Loss: 0.1196 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9879 Val AUC: 0.9697 Val PRC: 0.9719 Time: 0.24\n",
      "Epoch: 336 Train Loss: 0.1146 Acc: 0.9237 Pre: 0.9427 Recall: 0.9022 F1: 0.9220 Train AUC: 0.9901 Val AUC: 0.9675 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 337 Train Loss: 0.1184 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9898 Val AUC: 0.9688 Val PRC: 0.9725 Time: 0.24\n",
      "Epoch: 338 Train Loss: 0.1123 Acc: 0.9286 Pre: 0.9433 Recall: 0.9119 F1: 0.9274 Train AUC: 0.9897 Val AUC: 0.9701 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 339 Train Loss: 0.1163 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9891 Val AUC: 0.9716 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 340 Train Loss: 0.1024 Acc: 0.9256 Pre: 0.9394 Recall: 0.9100 F1: 0.9245 Train AUC: 0.9924 Val AUC: 0.9699 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 341 Train Loss: 0.1552 Acc: 0.9207 Pre: 0.9674 Recall: 0.8708 F1: 0.9166 Train AUC: 0.9859 Val AUC: 0.9696 Val PRC: 0.9733 Time: 0.24\n",
      "Epoch: 342 Train Loss: 0.1102 Acc: 0.9247 Pre: 0.9521 Recall: 0.8943 F1: 0.9223 Train AUC: 0.9905 Val AUC: 0.9725 Val PRC: 0.9750 Time: 0.25\n",
      "Epoch: 343 Train Loss: 0.1149 Acc: 0.9159 Pre: 0.9048 Recall: 0.9295 F1: 0.9170 Train AUC: 0.9893 Val AUC: 0.9715 Val PRC: 0.9751 Time: 0.24\n",
      "Epoch: 344 Train Loss: 0.1078 Acc: 0.9266 Pre: 0.9638 Recall: 0.8865 F1: 0.9235 Train AUC: 0.9913 Val AUC: 0.9718 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 345 Train Loss: 0.1090 Acc: 0.9198 Pre: 0.9281 Recall: 0.9100 F1: 0.9190 Train AUC: 0.9904 Val AUC: 0.9691 Val PRC: 0.9723 Time: 0.24\n",
      "Epoch: 346 Train Loss: 0.1170 Acc: 0.9247 Pre: 0.9357 Recall: 0.9119 F1: 0.9237 Train AUC: 0.9896 Val AUC: 0.9735 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 347 Train Loss: 0.1135 Acc: 0.9207 Pre: 0.9317 Recall: 0.9080 F1: 0.9197 Train AUC: 0.9898 Val AUC: 0.9690 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 348 Train Loss: 0.1083 Acc: 0.9374 Pre: 0.9497 Recall: 0.9237 F1: 0.9365 Train AUC: 0.9908 Val AUC: 0.9794 Val PRC: 0.9813 Time: 0.24\n",
      "Epoch: 349 Train Loss: 0.1042 Acc: 0.9295 Pre: 0.9434 Recall: 0.9139 F1: 0.9284 Train AUC: 0.9919 Val AUC: 0.9713 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 350 Train Loss: 0.1130 Acc: 0.9266 Pre: 0.9580 Recall: 0.8924 F1: 0.9240 Train AUC: 0.9900 Val AUC: 0.9727 Val PRC: 0.9760 Time: 0.24\n",
      "Epoch: 351 Train Loss: 0.1137 Acc: 0.9207 Pre: 0.9370 Recall: 0.9022 F1: 0.9192 Train AUC: 0.9895 Val AUC: 0.9709 Val PRC: 0.9746 Time: 0.42\n",
      "Epoch: 352 Train Loss: 0.1171 Acc: 0.9217 Pre: 0.9301 Recall: 0.9119 F1: 0.9209 Train AUC: 0.9882 Val AUC: 0.9694 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 353 Train Loss: 0.1094 Acc: 0.9217 Pre: 0.9371 Recall: 0.9041 F1: 0.9203 Train AUC: 0.9896 Val AUC: 0.9664 Val PRC: 0.9666 Time: 0.24\n",
      "Epoch: 354 Train Loss: 0.1217 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9884 Val AUC: 0.9702 Val PRC: 0.9735 Time: 0.24\n",
      "Epoch: 355 Train Loss: 0.1165 Acc: 0.9325 Pre: 0.9566 Recall: 0.9061 F1: 0.9307 Train AUC: 0.9888 Val AUC: 0.9699 Val PRC: 0.9707 Time: 0.24\n",
      "Epoch: 356 Train Loss: 0.1040 Acc: 0.9286 Pre: 0.9562 Recall: 0.8982 F1: 0.9263 Train AUC: 0.9912 Val AUC: 0.9685 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 357 Train Loss: 0.1005 Acc: 0.9217 Pre: 0.9389 Recall: 0.9022 F1: 0.9202 Train AUC: 0.9921 Val AUC: 0.9693 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 358 Train Loss: 0.1112 Acc: 0.9237 Pre: 0.9253 Recall: 0.9217 F1: 0.9235 Train AUC: 0.9900 Val AUC: 0.9716 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 359 Train Loss: 0.1139 Acc: 0.9207 Pre: 0.9183 Recall: 0.9237 F1: 0.9210 Train AUC: 0.9894 Val AUC: 0.9684 Val PRC: 0.9718 Time: 0.24\n",
      "Epoch: 360 Train Loss: 0.1134 Acc: 0.9256 Pre: 0.9412 Recall: 0.9080 F1: 0.9243 Train AUC: 0.9906 Val AUC: 0.9712 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 361 Train Loss: 0.1091 Acc: 0.9237 Pre: 0.9304 Recall: 0.9159 F1: 0.9231 Train AUC: 0.9900 Val AUC: 0.9709 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 362 Train Loss: 0.1245 Acc: 0.9256 Pre: 0.9448 Recall: 0.9041 F1: 0.9240 Train AUC: 0.9885 Val AUC: 0.9714 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 363 Train Loss: 0.1054 Acc: 0.9266 Pre: 0.9467 Recall: 0.9041 F1: 0.9249 Train AUC: 0.9913 Val AUC: 0.9728 Val PRC: 0.9766 Time: 0.25\n",
      "Epoch: 364 Train Loss: 0.1004 Acc: 0.9256 Pre: 0.9503 Recall: 0.8982 F1: 0.9235 Train AUC: 0.9924 Val AUC: 0.9739 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 365 Train Loss: 0.1159 Acc: 0.9198 Pre: 0.9333 Recall: 0.9041 F1: 0.9185 Train AUC: 0.9887 Val AUC: 0.9701 Val PRC: 0.9723 Time: 0.24\n",
      "Epoch: 366 Train Loss: 0.1066 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9906 Val AUC: 0.9720 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 367 Train Loss: 0.0912 Acc: 0.9237 Pre: 0.9464 Recall: 0.8982 F1: 0.9217 Train AUC: 0.9936 Val AUC: 0.9724 Val PRC: 0.9755 Time: 0.24\n",
      "Epoch: 368 Train Loss: 0.1075 Acc: 0.9198 Pre: 0.9086 Recall: 0.9335 F1: 0.9208 Train AUC: 0.9915 Val AUC: 0.9729 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 369 Train Loss: 0.1025 Acc: 0.9247 Pre: 0.9483 Recall: 0.8982 F1: 0.9226 Train AUC: 0.9911 Val AUC: 0.9722 Val PRC: 0.9756 Time: 0.24\n",
      "Epoch: 370 Train Loss: 0.0999 Acc: 0.9286 Pre: 0.9582 Recall: 0.8963 F1: 0.9262 Train AUC: 0.9922 Val AUC: 0.9709 Val PRC: 0.9733 Time: 0.24\n",
      "Epoch: 371 Train Loss: 0.1057 Acc: 0.9237 Pre: 0.9220 Recall: 0.9256 F1: 0.9238 Train AUC: 0.9905 Val AUC: 0.9714 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 372 Train Loss: 0.1025 Acc: 0.9207 Pre: 0.9300 Recall: 0.9100 F1: 0.9199 Train AUC: 0.9911 Val AUC: 0.9719 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 373 Train Loss: 0.1165 Acc: 0.9256 Pre: 0.9466 Recall: 0.9022 F1: 0.9238 Train AUC: 0.9888 Val AUC: 0.9716 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 374 Train Loss: 0.1073 Acc: 0.9295 Pre: 0.9621 Recall: 0.8943 F1: 0.9270 Train AUC: 0.9905 Val AUC: 0.9730 Val PRC: 0.9769 Time: 0.24\n",
      "Epoch: 375 Train Loss: 0.1045 Acc: 0.9217 Pre: 0.9250 Recall: 0.9178 F1: 0.9214 Train AUC: 0.9907 Val AUC: 0.9692 Val PRC: 0.9719 Time: 0.24\n",
      "Epoch: 376 Train Loss: 0.1010 Acc: 0.9237 Pre: 0.9374 Recall: 0.9080 F1: 0.9225 Train AUC: 0.9915 Val AUC: 0.9708 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 377 Train Loss: 0.1102 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9891 Val AUC: 0.9708 Val PRC: 0.9725 Time: 0.24\n",
      "Epoch: 378 Train Loss: 0.0986 Acc: 0.9227 Pre: 0.9426 Recall: 0.9002 F1: 0.9209 Train AUC: 0.9922 Val AUC: 0.9714 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 379 Train Loss: 0.1047 Acc: 0.9266 Pre: 0.9395 Recall: 0.9119 F1: 0.9255 Train AUC: 0.9908 Val AUC: 0.9692 Val PRC: 0.9733 Time: 0.24\n",
      "Epoch: 380 Train Loss: 0.0908 Acc: 0.9266 Pre: 0.9275 Recall: 0.9256 F1: 0.9265 Train AUC: 0.9930 Val AUC: 0.9687 Val PRC: 0.9709 Time: 0.24\n",
      "Epoch: 381 Train Loss: 0.1037 Acc: 0.9286 Pre: 0.9363 Recall: 0.9198 F1: 0.9279 Train AUC: 0.9897 Val AUC: 0.9680 Val PRC: 0.9682 Time: 0.24\n",
      "Epoch: 382 Train Loss: 0.1076 Acc: 0.9207 Pre: 0.9406 Recall: 0.8982 F1: 0.9189 Train AUC: 0.9899 Val AUC: 0.9680 Val PRC: 0.9716 Time: 0.24\n",
      "Epoch: 383 Train Loss: 0.1080 Acc: 0.9276 Pre: 0.9414 Recall: 0.9119 F1: 0.9264 Train AUC: 0.9897 Val AUC: 0.9692 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 384 Train Loss: 0.1070 Acc: 0.9227 Pre: 0.9372 Recall: 0.9061 F1: 0.9214 Train AUC: 0.9895 Val AUC: 0.9670 Val PRC: 0.9699 Time: 0.24\n",
      "Epoch: 385 Train Loss: 0.1059 Acc: 0.9178 Pre: 0.9228 Recall: 0.9119 F1: 0.9173 Train AUC: 0.9900 Val AUC: 0.9655 Val PRC: 0.9651 Time: 0.24\n",
      "Epoch: 386 Train Loss: 0.1046 Acc: 0.9266 Pre: 0.9542 Recall: 0.8963 F1: 0.9243 Train AUC: 0.9902 Val AUC: 0.9695 Val PRC: 0.9725 Time: 0.24\n",
      "Epoch: 387 Train Loss: 0.0993 Acc: 0.9276 Pre: 0.9505 Recall: 0.9022 F1: 0.9257 Train AUC: 0.9915 Val AUC: 0.9703 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 388 Train Loss: 0.1019 Acc: 0.9247 Pre: 0.9502 Recall: 0.8963 F1: 0.9225 Train AUC: 0.9915 Val AUC: 0.9698 Val PRC: 0.9744 Time: 0.25\n",
      "Epoch: 389 Train Loss: 0.1132 Acc: 0.9256 Pre: 0.9541 Recall: 0.8943 F1: 0.9232 Train AUC: 0.9890 Val AUC: 0.9706 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 390 Train Loss: 0.1023 Acc: 0.9237 Pre: 0.9482 Recall: 0.8963 F1: 0.9215 Train AUC: 0.9909 Val AUC: 0.9707 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 391 Train Loss: 0.1319 Acc: 0.9247 Pre: 0.9289 Recall: 0.9198 F1: 0.9243 Train AUC: 0.9892 Val AUC: 0.9716 Val PRC: 0.9738 Time: 0.24\n",
      "Epoch: 392 Train Loss: 0.1074 Acc: 0.9256 Pre: 0.9223 Recall: 0.9295 F1: 0.9259 Train AUC: 0.9898 Val AUC: 0.9731 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 393 Train Loss: 0.1053 Acc: 0.9227 Pre: 0.9252 Recall: 0.9198 F1: 0.9225 Train AUC: 0.9890 Val AUC: 0.9725 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 394 Train Loss: 0.1060 Acc: 0.9207 Pre: 0.9119 Recall: 0.9315 F1: 0.9216 Train AUC: 0.9904 Val AUC: 0.9710 Val PRC: 0.9734 Time: 0.24\n",
      "Epoch: 395 Train Loss: 0.0947 Acc: 0.9276 Pre: 0.9361 Recall: 0.9178 F1: 0.9269 Train AUC: 0.9932 Val AUC: 0.9706 Val PRC: 0.9755 Time: 0.24\n",
      "Epoch: 396 Train Loss: 0.0977 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9918 Val AUC: 0.9705 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 397 Train Loss: 0.1084 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9898 Val AUC: 0.9670 Val PRC: 0.9706 Time: 0.25\n",
      "Epoch: 398 Train Loss: 0.0971 Acc: 0.9276 Pre: 0.9310 Recall: 0.9237 F1: 0.9273 Train AUC: 0.9921 Val AUC: 0.9726 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 399 Train Loss: 0.1157 Acc: 0.9237 Pre: 0.9356 Recall: 0.9100 F1: 0.9226 Train AUC: 0.9898 Val AUC: 0.9715 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 400 Train Loss: 0.0933 Acc: 0.9159 Pre: 0.9110 Recall: 0.9217 F1: 0.9163 Train AUC: 0.9932 Val AUC: 0.9696 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 401 Train Loss: 0.1093 Acc: 0.9256 Pre: 0.9394 Recall: 0.9100 F1: 0.9245 Train AUC: 0.9910 Val AUC: 0.9721 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 402 Train Loss: 0.1173 Acc: 0.9237 Pre: 0.9558 Recall: 0.8885 F1: 0.9209 Train AUC: 0.9889 Val AUC: 0.9712 Val PRC: 0.9753 Time: 0.25\n",
      "Epoch: 403 Train Loss: 0.1147 Acc: 0.9198 Pre: 0.9459 Recall: 0.8904 F1: 0.9173 Train AUC: 0.9902 Val AUC: 0.9707 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 404 Train Loss: 0.1185 Acc: 0.9266 Pre: 0.9308 Recall: 0.9217 F1: 0.9263 Train AUC: 0.9880 Val AUC: 0.9718 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 405 Train Loss: 0.1092 Acc: 0.9256 Pre: 0.9256 Recall: 0.9256 F1: 0.9256 Train AUC: 0.9885 Val AUC: 0.9703 Val PRC: 0.9729 Time: 0.25\n",
      "Epoch: 406 Train Loss: 0.1004 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9909 Val AUC: 0.9698 Val PRC: 0.9737 Time: 0.25\n",
      "Epoch: 407 Train Loss: 0.1001 Acc: 0.9237 Pre: 0.9270 Recall: 0.9198 F1: 0.9234 Train AUC: 0.9912 Val AUC: 0.9697 Val PRC: 0.9728 Time: 0.25\n",
      "Epoch: 408 Train Loss: 0.0964 Acc: 0.9217 Pre: 0.9250 Recall: 0.9178 F1: 0.9214 Train AUC: 0.9925 Val AUC: 0.9683 Val PRC: 0.9726 Time: 0.25\n",
      "Epoch: 409 Train Loss: 0.0945 Acc: 0.9295 Pre: 0.9329 Recall: 0.9256 F1: 0.9293 Train AUC: 0.9925 Val AUC: 0.9711 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 410 Train Loss: 0.0884 Acc: 0.9237 Pre: 0.9270 Recall: 0.9198 F1: 0.9234 Train AUC: 0.9930 Val AUC: 0.9708 Val PRC: 0.9744 Time: 0.24\n",
      "Epoch: 411 Train Loss: 0.1026 Acc: 0.9237 Pre: 0.9558 Recall: 0.8885 F1: 0.9209 Train AUC: 0.9914 Val AUC: 0.9711 Val PRC: 0.9753 Time: 0.25\n",
      "Epoch: 412 Train Loss: 0.1106 Acc: 0.9168 Pre: 0.9096 Recall: 0.9256 F1: 0.9176 Train AUC: 0.9897 Val AUC: 0.9686 Val PRC: 0.9714 Time: 0.24\n",
      "Epoch: 413 Train Loss: 0.0921 Acc: 0.9256 Pre: 0.9485 Recall: 0.9002 F1: 0.9237 Train AUC: 0.9928 Val AUC: 0.9705 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 414 Train Loss: 0.1025 Acc: 0.9305 Pre: 0.9472 Recall: 0.9119 F1: 0.9292 Train AUC: 0.9918 Val AUC: 0.9709 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 415 Train Loss: 0.0969 Acc: 0.9227 Pre: 0.9202 Recall: 0.9256 F1: 0.9229 Train AUC: 0.9913 Val AUC: 0.9707 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 416 Train Loss: 0.1090 Acc: 0.9286 Pre: 0.9525 Recall: 0.9022 F1: 0.9266 Train AUC: 0.9897 Val AUC: 0.9704 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 417 Train Loss: 0.0946 Acc: 0.9217 Pre: 0.9575 Recall: 0.8826 F1: 0.9185 Train AUC: 0.9920 Val AUC: 0.9706 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 418 Train Loss: 0.0971 Acc: 0.9305 Pre: 0.9527 Recall: 0.9061 F1: 0.9288 Train AUC: 0.9918 Val AUC: 0.9737 Val PRC: 0.9779 Time: 0.24\n",
      "Epoch: 419 Train Loss: 0.0931 Acc: 0.9295 Pre: 0.9563 Recall: 0.9002 F1: 0.9274 Train AUC: 0.9928 Val AUC: 0.9726 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 420 Train Loss: 0.0901 Acc: 0.9256 Pre: 0.9394 Recall: 0.9100 F1: 0.9245 Train AUC: 0.9934 Val AUC: 0.9718 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 421 Train Loss: 0.0923 Acc: 0.9266 Pre: 0.9486 Recall: 0.9022 F1: 0.9248 Train AUC: 0.9930 Val AUC: 0.9718 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 422 Train Loss: 0.1088 Acc: 0.9119 Pre: 0.8994 Recall: 0.9276 F1: 0.9133 Train AUC: 0.9899 Val AUC: 0.9689 Val PRC: 0.9738 Time: 0.24\n",
      "Epoch: 423 Train Loss: 0.0977 Acc: 0.9256 Pre: 0.9359 Recall: 0.9139 F1: 0.9248 Train AUC: 0.9905 Val AUC: 0.9725 Val PRC: 0.9766 Time: 0.24\n",
      "Epoch: 424 Train Loss: 0.0919 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9923 Val AUC: 0.9727 Val PRC: 0.9765 Time: 0.25\n",
      "Epoch: 425 Train Loss: 0.1028 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9904 Val AUC: 0.9740 Val PRC: 0.9776 Time: 0.24\n",
      "Epoch: 426 Train Loss: 0.0934 Acc: 0.9276 Pre: 0.9432 Recall: 0.9100 F1: 0.9263 Train AUC: 0.9926 Val AUC: 0.9722 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 427 Train Loss: 0.0983 Acc: 0.9237 Pre: 0.9204 Recall: 0.9276 F1: 0.9240 Train AUC: 0.9922 Val AUC: 0.9740 Val PRC: 0.9770 Time: 0.23\n",
      "Epoch: 428 Train Loss: 0.1077 Acc: 0.9256 Pre: 0.9359 Recall: 0.9139 F1: 0.9248 Train AUC: 0.9902 Val AUC: 0.9711 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 429 Train Loss: 0.0983 Acc: 0.9149 Pre: 0.9061 Recall: 0.9256 F1: 0.9158 Train AUC: 0.9937 Val AUC: 0.9698 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 430 Train Loss: 0.1089 Acc: 0.9247 Pre: 0.9559 Recall: 0.8904 F1: 0.9220 Train AUC: 0.9899 Val AUC: 0.9721 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 431 Train Loss: 0.0921 Acc: 0.9237 Pre: 0.9287 Recall: 0.9178 F1: 0.9232 Train AUC: 0.9937 Val AUC: 0.9712 Val PRC: 0.9746 Time: 0.23\n",
      "Epoch: 432 Train Loss: 0.0935 Acc: 0.9198 Pre: 0.9264 Recall: 0.9119 F1: 0.9191 Train AUC: 0.9921 Val AUC: 0.9714 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 433 Train Loss: 0.0903 Acc: 0.9217 Pre: 0.9575 Recall: 0.8826 F1: 0.9185 Train AUC: 0.9940 Val AUC: 0.9672 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 434 Train Loss: 0.0961 Acc: 0.9237 Pre: 0.9304 Recall: 0.9159 F1: 0.9231 Train AUC: 0.9917 Val AUC: 0.9675 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 435 Train Loss: 0.0926 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9926 Val AUC: 0.9710 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 436 Train Loss: 0.0963 Acc: 0.9188 Pre: 0.9213 Recall: 0.9159 F1: 0.9185 Train AUC: 0.9924 Val AUC: 0.9696 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 437 Train Loss: 0.0977 Acc: 0.9217 Pre: 0.9234 Recall: 0.9198 F1: 0.9216 Train AUC: 0.9920 Val AUC: 0.9694 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 438 Train Loss: 0.0936 Acc: 0.9266 Pre: 0.9504 Recall: 0.9002 F1: 0.9246 Train AUC: 0.9923 Val AUC: 0.9699 Val PRC: 0.9712 Time: 0.23\n",
      "Epoch: 439 Train Loss: 0.0985 Acc: 0.9227 Pre: 0.9303 Recall: 0.9139 F1: 0.9220 Train AUC: 0.9916 Val AUC: 0.9716 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 440 Train Loss: 0.0938 Acc: 0.9295 Pre: 0.9452 Recall: 0.9119 F1: 0.9283 Train AUC: 0.9927 Val AUC: 0.9734 Val PRC: 0.9772 Time: 0.23\n",
      "Epoch: 441 Train Loss: 0.1024 Acc: 0.9237 Pre: 0.9656 Recall: 0.8787 F1: 0.9201 Train AUC: 0.9900 Val AUC: 0.9717 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 442 Train Loss: 0.1063 Acc: 0.9276 Pre: 0.9679 Recall: 0.8845 F1: 0.9243 Train AUC: 0.9888 Val AUC: 0.9724 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 443 Train Loss: 0.1074 Acc: 0.9237 Pre: 0.9124 Recall: 0.9374 F1: 0.9247 Train AUC: 0.9893 Val AUC: 0.9734 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 444 Train Loss: 0.1021 Acc: 0.9256 Pre: 0.9448 Recall: 0.9041 F1: 0.9240 Train AUC: 0.9904 Val AUC: 0.9709 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 445 Train Loss: 0.0963 Acc: 0.9247 Pre: 0.9340 Recall: 0.9139 F1: 0.9238 Train AUC: 0.9919 Val AUC: 0.9710 Val PRC: 0.9735 Time: 0.23\n",
      "Epoch: 446 Train Loss: 0.0966 Acc: 0.9247 Pre: 0.9189 Recall: 0.9315 F1: 0.9252 Train AUC: 0.9921 Val AUC: 0.9709 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 447 Train Loss: 0.0859 Acc: 0.9305 Pre: 0.9545 Recall: 0.9041 F1: 0.9286 Train AUC: 0.9942 Val AUC: 0.9719 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 448 Train Loss: 0.0875 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9933 Val AUC: 0.9687 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 449 Train Loss: 0.1156 Acc: 0.9286 Pre: 0.9363 Recall: 0.9198 F1: 0.9279 Train AUC: 0.9893 Val AUC: 0.9739 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 450 Train Loss: 0.0936 Acc: 0.9266 Pre: 0.9542 Recall: 0.8963 F1: 0.9243 Train AUC: 0.9930 Val AUC: 0.9708 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 451 Train Loss: 0.0914 Acc: 0.9286 Pre: 0.9433 Recall: 0.9119 F1: 0.9274 Train AUC: 0.9919 Val AUC: 0.9734 Val PRC: 0.9780 Time: 0.23\n",
      "Epoch: 452 Train Loss: 0.0924 Acc: 0.9276 Pre: 0.9396 Recall: 0.9139 F1: 0.9266 Train AUC: 0.9926 Val AUC: 0.9698 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 453 Train Loss: 0.1098 Acc: 0.9266 Pre: 0.9308 Recall: 0.9217 F1: 0.9263 Train AUC: 0.9909 Val AUC: 0.9709 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 454 Train Loss: 0.0943 Acc: 0.9217 Pre: 0.9371 Recall: 0.9041 F1: 0.9203 Train AUC: 0.9914 Val AUC: 0.9720 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 455 Train Loss: 0.0981 Acc: 0.9286 Pre: 0.9525 Recall: 0.9022 F1: 0.9266 Train AUC: 0.9918 Val AUC: 0.9709 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 456 Train Loss: 0.0986 Acc: 0.9247 Pre: 0.9357 Recall: 0.9119 F1: 0.9237 Train AUC: 0.9900 Val AUC: 0.9710 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 457 Train Loss: 0.0972 Acc: 0.9207 Pre: 0.9300 Recall: 0.9100 F1: 0.9199 Train AUC: 0.9913 Val AUC: 0.9664 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 458 Train Loss: 0.1007 Acc: 0.9315 Pre: 0.9603 Recall: 0.9002 F1: 0.9293 Train AUC: 0.9903 Val AUC: 0.9708 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 459 Train Loss: 0.0786 Acc: 0.9207 Pre: 0.9442 Recall: 0.8943 F1: 0.9186 Train AUC: 0.9945 Val AUC: 0.9668 Val PRC: 0.9729 Time: 0.23\n",
      "Epoch: 460 Train Loss: 0.0948 Acc: 0.9237 Pre: 0.9391 Recall: 0.9061 F1: 0.9223 Train AUC: 0.9922 Val AUC: 0.9673 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 461 Train Loss: 0.0914 Acc: 0.9227 Pre: 0.9444 Recall: 0.8982 F1: 0.9208 Train AUC: 0.9929 Val AUC: 0.9691 Val PRC: 0.9733 Time: 0.25\n",
      "Epoch: 462 Train Loss: 0.0934 Acc: 0.9217 Pre: 0.9499 Recall: 0.8904 F1: 0.9192 Train AUC: 0.9924 Val AUC: 0.9681 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 463 Train Loss: 0.1093 Acc: 0.9286 Pre: 0.9620 Recall: 0.8924 F1: 0.9259 Train AUC: 0.9880 Val AUC: 0.9719 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 464 Train Loss: 0.0896 Acc: 0.9286 Pre: 0.9488 Recall: 0.9061 F1: 0.9269 Train AUC: 0.9913 Val AUC: 0.9711 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 465 Train Loss: 0.0885 Acc: 0.9247 Pre: 0.9110 Recall: 0.9413 F1: 0.9259 Train AUC: 0.9926 Val AUC: 0.9720 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 466 Train Loss: 0.1014 Acc: 0.9256 Pre: 0.9223 Recall: 0.9295 F1: 0.9259 Train AUC: 0.9897 Val AUC: 0.9708 Val PRC: 0.9751 Time: 0.24\n",
      "Epoch: 467 Train Loss: 0.0802 Acc: 0.9247 Pre: 0.9289 Recall: 0.9198 F1: 0.9243 Train AUC: 0.9945 Val AUC: 0.9707 Val PRC: 0.9754 Time: 0.24\n",
      "Epoch: 468 Train Loss: 0.0965 Acc: 0.9217 Pre: 0.9462 Recall: 0.8943 F1: 0.9195 Train AUC: 0.9917 Val AUC: 0.9700 Val PRC: 0.9746 Time: 0.25\n",
      "Epoch: 469 Train Loss: 0.1182 Acc: 0.9266 Pre: 0.9413 Recall: 0.9100 F1: 0.9254 Train AUC: 0.9920 Val AUC: 0.9709 Val PRC: 0.9770 Time: 0.24\n",
      "Epoch: 470 Train Loss: 0.0866 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9938 Val AUC: 0.9690 Val PRC: 0.9749 Time: 0.24\n",
      "Epoch: 471 Train Loss: 0.0829 Acc: 0.9276 Pre: 0.9543 Recall: 0.8982 F1: 0.9254 Train AUC: 0.9941 Val AUC: 0.9688 Val PRC: 0.9746 Time: 0.24\n",
      "Epoch: 472 Train Loss: 0.0874 Acc: 0.9237 Pre: 0.9501 Recall: 0.8943 F1: 0.9214 Train AUC: 0.9931 Val AUC: 0.9719 Val PRC: 0.9752 Time: 0.24\n",
      "Epoch: 473 Train Loss: 0.0981 Acc: 0.9247 Pre: 0.9411 Recall: 0.9061 F1: 0.9232 Train AUC: 0.9919 Val AUC: 0.9706 Val PRC: 0.9734 Time: 0.24\n",
      "Epoch: 474 Train Loss: 0.1073 Acc: 0.9237 Pre: 0.9558 Recall: 0.8885 F1: 0.9209 Train AUC: 0.9903 Val AUC: 0.9715 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 475 Train Loss: 0.0816 Acc: 0.9286 Pre: 0.9582 Recall: 0.8963 F1: 0.9262 Train AUC: 0.9938 Val AUC: 0.9735 Val PRC: 0.9774 Time: 0.25\n",
      "Epoch: 476 Train Loss: 0.1069 Acc: 0.9217 Pre: 0.9152 Recall: 0.9295 F1: 0.9223 Train AUC: 0.9939 Val AUC: 0.9722 Val PRC: 0.9751 Time: 0.24\n",
      "Epoch: 477 Train Loss: 0.0872 Acc: 0.9295 Pre: 0.9434 Recall: 0.9139 F1: 0.9284 Train AUC: 0.9929 Val AUC: 0.9710 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 478 Train Loss: 0.0872 Acc: 0.9237 Pre: 0.9409 Recall: 0.9041 F1: 0.9222 Train AUC: 0.9934 Val AUC: 0.9719 Val PRC: 0.9763 Time: 0.25\n",
      "Epoch: 479 Train Loss: 0.0859 Acc: 0.9247 Pre: 0.9306 Recall: 0.9178 F1: 0.9241 Train AUC: 0.9936 Val AUC: 0.9704 Val PRC: 0.9753 Time: 0.24\n",
      "Epoch: 480 Train Loss: 0.0903 Acc: 0.9227 Pre: 0.9320 Recall: 0.9119 F1: 0.9219 Train AUC: 0.9927 Val AUC: 0.9685 Val PRC: 0.9733 Time: 0.24\n",
      "Epoch: 481 Train Loss: 0.0931 Acc: 0.9247 Pre: 0.9411 Recall: 0.9061 F1: 0.9232 Train AUC: 0.9927 Val AUC: 0.9703 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 482 Train Loss: 0.0918 Acc: 0.9266 Pre: 0.9486 Recall: 0.9022 F1: 0.9248 Train AUC: 0.9923 Val AUC: 0.9696 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 483 Train Loss: 0.0852 Acc: 0.9198 Pre: 0.9086 Recall: 0.9335 F1: 0.9208 Train AUC: 0.9941 Val AUC: 0.9699 Val PRC: 0.9740 Time: 0.23\n",
      "Epoch: 484 Train Loss: 0.0906 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9929 Val AUC: 0.9707 Val PRC: 0.9755 Time: 0.41\n",
      "Epoch: 485 Train Loss: 0.0842 Acc: 0.9217 Pre: 0.9319 Recall: 0.9100 F1: 0.9208 Train AUC: 0.9936 Val AUC: 0.9722 Val PRC: 0.9759 Time: 0.24\n",
      "Epoch: 486 Train Loss: 0.0911 Acc: 0.9256 Pre: 0.9448 Recall: 0.9041 F1: 0.9240 Train AUC: 0.9929 Val AUC: 0.9729 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 487 Train Loss: 0.0849 Acc: 0.9266 Pre: 0.9395 Recall: 0.9119 F1: 0.9255 Train AUC: 0.9932 Val AUC: 0.9705 Val PRC: 0.9703 Time: 0.24\n",
      "Epoch: 488 Train Loss: 0.1028 Acc: 0.9266 Pre: 0.9542 Recall: 0.8963 F1: 0.9243 Train AUC: 0.9898 Val AUC: 0.9704 Val PRC: 0.9733 Time: 0.24\n",
      "Epoch: 489 Train Loss: 0.0960 Acc: 0.9276 Pre: 0.9259 Recall: 0.9295 F1: 0.9277 Train AUC: 0.9915 Val AUC: 0.9740 Val PRC: 0.9777 Time: 0.23\n",
      "Epoch: 490 Train Loss: 0.0857 Acc: 0.9305 Pre: 0.9583 Recall: 0.9002 F1: 0.9284 Train AUC: 0.9933 Val AUC: 0.9729 Val PRC: 0.9769 Time: 0.24\n",
      "Epoch: 491 Train Loss: 0.0857 Acc: 0.9315 Pre: 0.9584 Recall: 0.9022 F1: 0.9294 Train AUC: 0.9935 Val AUC: 0.9713 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 492 Train Loss: 0.0808 Acc: 0.9295 Pre: 0.9470 Recall: 0.9100 F1: 0.9281 Train AUC: 0.9943 Val AUC: 0.9702 Val PRC: 0.9742 Time: 0.24\n",
      "Epoch: 493 Train Loss: 0.0948 Acc: 0.9266 Pre: 0.9449 Recall: 0.9061 F1: 0.9251 Train AUC: 0.9918 Val AUC: 0.9710 Val PRC: 0.9753 Time: 0.24\n",
      "Epoch: 494 Train Loss: 0.1094 Acc: 0.9305 Pre: 0.9453 Recall: 0.9139 F1: 0.9294 Train AUC: 0.9906 Val AUC: 0.9697 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 495 Train Loss: 0.0897 Acc: 0.9256 Pre: 0.9485 Recall: 0.9002 F1: 0.9237 Train AUC: 0.9919 Val AUC: 0.9687 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 496 Train Loss: 0.0905 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9919 Val AUC: 0.9686 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 497 Train Loss: 0.0862 Acc: 0.9276 Pre: 0.9505 Recall: 0.9022 F1: 0.9257 Train AUC: 0.9936 Val AUC: 0.9713 Val PRC: 0.9737 Time: 0.23\n",
      "Epoch: 498 Train Loss: 0.0879 Acc: 0.9315 Pre: 0.9473 Recall: 0.9139 F1: 0.9303 Train AUC: 0.9915 Val AUC: 0.9703 Val PRC: 0.9713 Time: 0.25\n",
      "Epoch: 499 Train Loss: 0.0884 Acc: 0.9207 Pre: 0.9283 Recall: 0.9119 F1: 0.9200 Train AUC: 0.9920 Val AUC: 0.9704 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 500 Train Loss: 0.0995 Acc: 0.9256 Pre: 0.9485 Recall: 0.9002 F1: 0.9237 Train AUC: 0.9947 Val AUC: 0.9707 Val PRC: 0.9766 Time: 0.23\n",
      "Fold: 4 Best Epoch: 348 Val acc: 0.9374 Val Pre: 0.9497 Val Recall: 0.9237 Val F1: 0.9365 Val AUC: 0.9794 Val PRC: 0.9813\n",
      "------this is 5th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6983 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.5119 Val AUC: 0.5166 Val PRC: 0.5133 Time: 0.24\n",
      "Epoch: 2 Train Loss: 0.7175 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.4062 Val AUC: 0.4003 Val PRC: 0.4359 Time: 0.23\n",
      "Epoch: 3 Train Loss: 0.7069 Acc: 0.5000 Pre: 0.5000 Recall: 0.9980 F1: 0.6662 Train AUC: 0.4582 Val AUC: 0.4603 Val PRC: 0.4929 Time: 0.24\n",
      "Epoch: 4 Train Loss: 0.7131 Acc: 0.4990 Pre: 0.4995 Recall: 0.9980 F1: 0.6658 Train AUC: 0.4235 Val AUC: 0.4372 Val PRC: 0.4743 Time: 0.23\n",
      "Epoch: 5 Train Loss: 0.6969 Acc: 0.5274 Pre: 0.5150 Recall: 0.9432 F1: 0.6662 Train AUC: 0.5110 Val AUC: 0.5353 Val PRC: 0.5190 Time: 0.24\n",
      "Epoch: 6 Train Loss: 0.6881 Acc: 0.5010 Pre: 0.5005 Recall: 0.9980 F1: 0.6667 Train AUC: 0.5492 Val AUC: 0.5458 Val PRC: 0.5440 Time: 0.23\n",
      "Epoch: 7 Train Loss: 0.6643 Acc: 0.5861 Pre: 0.5516 Recall: 0.9198 F1: 0.6897 Train AUC: 0.6920 Val AUC: 0.6673 Val PRC: 0.6349 Time: 0.23\n",
      "Epoch: 8 Train Loss: 0.6526 Acc: 0.6389 Pre: 0.5962 Recall: 0.8611 F1: 0.7046 Train AUC: 0.7368 Val AUC: 0.7290 Val PRC: 0.7093 Time: 0.23\n",
      "Epoch: 9 Train Loss: 0.6482 Acc: 0.6448 Pre: 0.6042 Recall: 0.8395 F1: 0.7027 Train AUC: 0.7334 Val AUC: 0.7389 Val PRC: 0.7290 Time: 0.23\n",
      "Epoch: 10 Train Loss: 0.6432 Acc: 0.6419 Pre: 0.5955 Recall: 0.8845 F1: 0.7118 Train AUC: 0.7426 Val AUC: 0.7579 Val PRC: 0.7517 Time: 0.23\n",
      "Epoch: 11 Train Loss: 0.6222 Acc: 0.7153 Pre: 0.6622 Recall: 0.8787 F1: 0.7553 Train AUC: 0.8204 Val AUC: 0.8040 Val PRC: 0.7686 Time: 0.23\n",
      "Epoch: 12 Train Loss: 0.6249 Acc: 0.6791 Pre: 0.6336 Recall: 0.8493 F1: 0.7258 Train AUC: 0.7841 Val AUC: 0.7759 Val PRC: 0.7783 Time: 0.24\n",
      "Epoch: 13 Train Loss: 0.6094 Acc: 0.7143 Pre: 0.6562 Recall: 0.9002 F1: 0.7591 Train AUC: 0.8297 Val AUC: 0.8126 Val PRC: 0.7905 Time: 0.23\n",
      "Epoch: 14 Train Loss: 0.5983 Acc: 0.7564 Pre: 0.7399 Recall: 0.7906 F1: 0.7644 Train AUC: 0.8323 Val AUC: 0.8207 Val PRC: 0.8008 Time: 0.23\n",
      "Epoch: 15 Train Loss: 0.5918 Acc: 0.7182 Pre: 0.6682 Recall: 0.8669 F1: 0.7547 Train AUC: 0.8326 Val AUC: 0.8223 Val PRC: 0.8081 Time: 0.23\n",
      "Epoch: 16 Train Loss: 0.5788 Acc: 0.7573 Pre: 0.7271 Recall: 0.8239 F1: 0.7725 Train AUC: 0.8438 Val AUC: 0.8347 Val PRC: 0.8177 Time: 0.23\n",
      "Epoch: 17 Train Loss: 0.5743 Acc: 0.7348 Pre: 0.6714 Recall: 0.9198 F1: 0.7762 Train AUC: 0.8432 Val AUC: 0.8279 Val PRC: 0.8178 Time: 0.23\n",
      "Epoch: 18 Train Loss: 0.5510 Acc: 0.7378 Pre: 0.6709 Recall: 0.9335 F1: 0.7807 Train AUC: 0.8629 Val AUC: 0.8600 Val PRC: 0.8520 Time: 0.23\n",
      "Epoch: 19 Train Loss: 0.5456 Acc: 0.7290 Pre: 0.6667 Recall: 0.9159 F1: 0.7716 Train AUC: 0.8597 Val AUC: 0.8415 Val PRC: 0.8354 Time: 0.23\n",
      "Epoch: 20 Train Loss: 0.5454 Acc: 0.7652 Pre: 0.7468 Recall: 0.8023 F1: 0.7736 Train AUC: 0.8641 Val AUC: 0.8540 Val PRC: 0.8419 Time: 0.23\n",
      "Epoch: 21 Train Loss: 0.5470 Acc: 0.7485 Pre: 0.6879 Recall: 0.9100 F1: 0.7835 Train AUC: 0.8539 Val AUC: 0.8582 Val PRC: 0.8446 Time: 0.23\n",
      "Epoch: 22 Train Loss: 0.5496 Acc: 0.7534 Pre: 0.6913 Recall: 0.9159 F1: 0.7879 Train AUC: 0.8328 Val AUC: 0.8414 Val PRC: 0.8430 Time: 0.23\n",
      "Epoch: 23 Train Loss: 0.5039 Acc: 0.7642 Pre: 0.7136 Recall: 0.8826 F1: 0.7892 Train AUC: 0.8759 Val AUC: 0.8688 Val PRC: 0.8747 Time: 0.23\n",
      "Epoch: 24 Train Loss: 0.4814 Acc: 0.7740 Pre: 0.7448 Recall: 0.8337 F1: 0.7867 Train AUC: 0.8828 Val AUC: 0.8702 Val PRC: 0.8784 Time: 0.24\n",
      "Epoch: 25 Train Loss: 0.4880 Acc: 0.7740 Pre: 0.7310 Recall: 0.8669 F1: 0.7932 Train AUC: 0.8800 Val AUC: 0.8727 Val PRC: 0.8839 Time: 0.24\n",
      "Epoch: 26 Train Loss: 0.4687 Acc: 0.7740 Pre: 0.7128 Recall: 0.9178 F1: 0.8024 Train AUC: 0.8924 Val AUC: 0.8846 Val PRC: 0.8964 Time: 0.23\n",
      "Epoch: 27 Train Loss: 0.4700 Acc: 0.7994 Pre: 0.7823 Recall: 0.8297 F1: 0.8053 Train AUC: 0.8815 Val AUC: 0.8835 Val PRC: 0.8967 Time: 0.23\n",
      "Epoch: 28 Train Loss: 0.4545 Acc: 0.8092 Pre: 0.8376 Recall: 0.7671 F1: 0.8008 Train AUC: 0.8870 Val AUC: 0.8867 Val PRC: 0.9022 Time: 0.23\n",
      "Epoch: 29 Train Loss: 0.4577 Acc: 0.8004 Pre: 0.7766 Recall: 0.8434 F1: 0.8086 Train AUC: 0.8894 Val AUC: 0.8884 Val PRC: 0.8985 Time: 0.23\n",
      "Epoch: 30 Train Loss: 0.4428 Acc: 0.8219 Pre: 0.8434 Recall: 0.7906 F1: 0.8162 Train AUC: 0.8971 Val AUC: 0.8949 Val PRC: 0.9090 Time: 0.24\n",
      "Epoch: 31 Train Loss: 0.4221 Acc: 0.8151 Pre: 0.8286 Recall: 0.7945 F1: 0.8112 Train AUC: 0.9016 Val AUC: 0.8982 Val PRC: 0.9110 Time: 0.24\n",
      "Epoch: 32 Train Loss: 0.4279 Acc: 0.8004 Pre: 0.7786 Recall: 0.8395 F1: 0.8079 Train AUC: 0.8969 Val AUC: 0.8871 Val PRC: 0.9074 Time: 0.23\n",
      "Epoch: 33 Train Loss: 0.4195 Acc: 0.8249 Pre: 0.8609 Recall: 0.7750 F1: 0.8157 Train AUC: 0.9000 Val AUC: 0.8994 Val PRC: 0.9157 Time: 0.23\n",
      "Epoch: 34 Train Loss: 0.4229 Acc: 0.8327 Pre: 0.8556 Recall: 0.8004 F1: 0.8271 Train AUC: 0.9004 Val AUC: 0.8992 Val PRC: 0.9157 Time: 0.23\n",
      "Epoch: 35 Train Loss: 0.4094 Acc: 0.8014 Pre: 0.8105 Recall: 0.7867 F1: 0.7984 Train AUC: 0.8997 Val AUC: 0.8897 Val PRC: 0.9063 Time: 0.23\n",
      "Epoch: 36 Train Loss: 0.4065 Acc: 0.8151 Pre: 0.8382 Recall: 0.7808 F1: 0.8085 Train AUC: 0.9045 Val AUC: 0.9011 Val PRC: 0.9166 Time: 0.23\n",
      "Epoch: 37 Train Loss: 0.3908 Acc: 0.8229 Pre: 0.8395 Recall: 0.7984 F1: 0.8185 Train AUC: 0.9120 Val AUC: 0.9039 Val PRC: 0.9163 Time: 0.23\n",
      "Epoch: 38 Train Loss: 0.4028 Acc: 0.8151 Pre: 0.8026 Recall: 0.8356 F1: 0.8188 Train AUC: 0.9058 Val AUC: 0.9001 Val PRC: 0.9125 Time: 0.23\n",
      "Epoch: 39 Train Loss: 0.3901 Acc: 0.8209 Pre: 0.8083 Recall: 0.8415 F1: 0.8245 Train AUC: 0.9078 Val AUC: 0.9060 Val PRC: 0.9217 Time: 0.23\n",
      "Epoch: 40 Train Loss: 0.3747 Acc: 0.8395 Pre: 0.8916 Recall: 0.7730 F1: 0.8281 Train AUC: 0.9159 Val AUC: 0.9116 Val PRC: 0.9260 Time: 0.24\n",
      "Epoch: 41 Train Loss: 0.3876 Acc: 0.8297 Pre: 0.8432 Recall: 0.8102 F1: 0.8263 Train AUC: 0.9107 Val AUC: 0.9065 Val PRC: 0.9204 Time: 0.24\n",
      "Epoch: 42 Train Loss: 0.3847 Acc: 0.8180 Pre: 0.7971 Recall: 0.8532 F1: 0.8242 Train AUC: 0.9087 Val AUC: 0.9086 Val PRC: 0.9222 Time: 0.23\n",
      "Epoch: 43 Train Loss: 0.3709 Acc: 0.8209 Pre: 0.8166 Recall: 0.8278 F1: 0.8222 Train AUC: 0.9144 Val AUC: 0.9062 Val PRC: 0.9200 Time: 0.23\n",
      "Epoch: 44 Train Loss: 0.3605 Acc: 0.8053 Pre: 0.7836 Recall: 0.8434 F1: 0.8124 Train AUC: 0.9131 Val AUC: 0.9086 Val PRC: 0.9214 Time: 0.23\n",
      "Epoch: 45 Train Loss: 0.3530 Acc: 0.8327 Pre: 0.8664 Recall: 0.7867 F1: 0.8246 Train AUC: 0.9173 Val AUC: 0.9130 Val PRC: 0.9200 Time: 0.23\n",
      "Epoch: 46 Train Loss: 0.3442 Acc: 0.8141 Pre: 0.7934 Recall: 0.8493 F1: 0.8204 Train AUC: 0.9252 Val AUC: 0.9126 Val PRC: 0.9228 Time: 0.23\n",
      "Epoch: 47 Train Loss: 0.3309 Acc: 0.8415 Pre: 0.8852 Recall: 0.7847 F1: 0.8320 Train AUC: 0.9299 Val AUC: 0.9198 Val PRC: 0.9238 Time: 0.23\n",
      "Epoch: 48 Train Loss: 0.3335 Acc: 0.8249 Pre: 0.8007 Recall: 0.8650 F1: 0.8316 Train AUC: 0.9292 Val AUC: 0.9210 Val PRC: 0.9269 Time: 0.23\n",
      "Epoch: 49 Train Loss: 0.3400 Acc: 0.8415 Pre: 0.8375 Recall: 0.8474 F1: 0.8424 Train AUC: 0.9275 Val AUC: 0.9198 Val PRC: 0.9310 Time: 0.23\n",
      "Epoch: 50 Train Loss: 0.3085 Acc: 0.8562 Pre: 0.8872 Recall: 0.8160 F1: 0.8502 Train AUC: 0.9405 Val AUC: 0.9248 Val PRC: 0.9307 Time: 0.24\n",
      "Epoch: 51 Train Loss: 0.3141 Acc: 0.8493 Pre: 0.8592 Recall: 0.8356 F1: 0.8472 Train AUC: 0.9363 Val AUC: 0.9239 Val PRC: 0.9330 Time: 0.23\n",
      "Epoch: 52 Train Loss: 0.3143 Acc: 0.8542 Pre: 0.8521 Recall: 0.8571 F1: 0.8546 Train AUC: 0.9389 Val AUC: 0.9294 Val PRC: 0.9334 Time: 0.23\n",
      "Epoch: 53 Train Loss: 0.3060 Acc: 0.8552 Pre: 0.8821 Recall: 0.8200 F1: 0.8499 Train AUC: 0.9406 Val AUC: 0.9319 Val PRC: 0.9370 Time: 0.23\n",
      "Epoch: 54 Train Loss: 0.3039 Acc: 0.8571 Pre: 0.8717 Recall: 0.8376 F1: 0.8543 Train AUC: 0.9430 Val AUC: 0.9321 Val PRC: 0.9407 Time: 0.24\n",
      "Epoch: 55 Train Loss: 0.2996 Acc: 0.8493 Pre: 0.8822 Recall: 0.8063 F1: 0.8425 Train AUC: 0.9419 Val AUC: 0.9308 Val PRC: 0.9387 Time: 0.24\n",
      "Epoch: 56 Train Loss: 0.2978 Acc: 0.8513 Pre: 0.8597 Recall: 0.8395 F1: 0.8495 Train AUC: 0.9435 Val AUC: 0.9324 Val PRC: 0.9350 Time: 0.23\n",
      "Epoch: 57 Train Loss: 0.2898 Acc: 0.8493 Pre: 0.8480 Recall: 0.8513 F1: 0.8496 Train AUC: 0.9461 Val AUC: 0.9343 Val PRC: 0.9418 Time: 0.23\n",
      "Epoch: 58 Train Loss: 0.2971 Acc: 0.8581 Pre: 0.8828 Recall: 0.8258 F1: 0.8534 Train AUC: 0.9436 Val AUC: 0.9363 Val PRC: 0.9422 Time: 0.23\n",
      "Epoch: 59 Train Loss: 0.2771 Acc: 0.8532 Pre: 0.8264 Recall: 0.8943 F1: 0.8590 Train AUC: 0.9510 Val AUC: 0.9420 Val PRC: 0.9471 Time: 0.23\n",
      "Epoch: 60 Train Loss: 0.2844 Acc: 0.8620 Pre: 0.8730 Recall: 0.8474 F1: 0.8600 Train AUC: 0.9485 Val AUC: 0.9429 Val PRC: 0.9471 Time: 0.23\n",
      "Epoch: 61 Train Loss: 0.2810 Acc: 0.8611 Pre: 0.8868 Recall: 0.8278 F1: 0.8563 Train AUC: 0.9501 Val AUC: 0.9367 Val PRC: 0.9413 Time: 0.23\n",
      "Epoch: 62 Train Loss: 0.2771 Acc: 0.8532 Pre: 0.8336 Recall: 0.8826 F1: 0.8574 Train AUC: 0.9515 Val AUC: 0.9406 Val PRC: 0.9452 Time: 0.23\n",
      "Epoch: 63 Train Loss: 0.2821 Acc: 0.8434 Pre: 0.8140 Recall: 0.8904 F1: 0.8505 Train AUC: 0.9493 Val AUC: 0.9368 Val PRC: 0.9428 Time: 0.23\n",
      "Epoch: 64 Train Loss: 0.2849 Acc: 0.8640 Pre: 0.8444 Recall: 0.8924 F1: 0.8677 Train AUC: 0.9473 Val AUC: 0.9400 Val PRC: 0.9463 Time: 0.23\n",
      "Epoch: 65 Train Loss: 0.2849 Acc: 0.8679 Pre: 0.8806 Recall: 0.8513 F1: 0.8657 Train AUC: 0.9478 Val AUC: 0.9411 Val PRC: 0.9465 Time: 0.23\n",
      "Epoch: 66 Train Loss: 0.2670 Acc: 0.8601 Pre: 0.8636 Recall: 0.8552 F1: 0.8594 Train AUC: 0.9540 Val AUC: 0.9427 Val PRC: 0.9465 Time: 0.23\n",
      "Epoch: 67 Train Loss: 0.2631 Acc: 0.8571 Pre: 0.8324 Recall: 0.8943 F1: 0.8623 Train AUC: 0.9553 Val AUC: 0.9411 Val PRC: 0.9423 Time: 0.25\n",
      "Epoch: 68 Train Loss: 0.2634 Acc: 0.8581 Pre: 0.8401 Recall: 0.8845 F1: 0.8618 Train AUC: 0.9566 Val AUC: 0.9418 Val PRC: 0.9465 Time: 0.25\n",
      "Epoch: 69 Train Loss: 0.2535 Acc: 0.8679 Pre: 0.8629 Recall: 0.8748 F1: 0.8688 Train AUC: 0.9590 Val AUC: 0.9463 Val PRC: 0.9493 Time: 0.23\n",
      "Epoch: 70 Train Loss: 0.2495 Acc: 0.8659 Pre: 0.8710 Recall: 0.8591 F1: 0.8650 Train AUC: 0.9602 Val AUC: 0.9427 Val PRC: 0.9423 Time: 0.23\n",
      "Epoch: 71 Train Loss: 0.2609 Acc: 0.8708 Pre: 0.9058 Recall: 0.8278 F1: 0.8650 Train AUC: 0.9572 Val AUC: 0.9434 Val PRC: 0.9471 Time: 0.24\n",
      "Epoch: 72 Train Loss: 0.2601 Acc: 0.8699 Pre: 0.8474 Recall: 0.9022 F1: 0.8739 Train AUC: 0.9565 Val AUC: 0.9474 Val PRC: 0.9503 Time: 0.24\n",
      "Epoch: 73 Train Loss: 0.2634 Acc: 0.8630 Pre: 0.8922 Recall: 0.8258 F1: 0.8577 Train AUC: 0.9581 Val AUC: 0.9421 Val PRC: 0.9448 Time: 0.23\n",
      "Epoch: 74 Train Loss: 0.2537 Acc: 0.8689 Pre: 0.8733 Recall: 0.8630 F1: 0.8681 Train AUC: 0.9592 Val AUC: 0.9491 Val PRC: 0.9488 Time: 0.24\n",
      "Epoch: 75 Train Loss: 0.2629 Acc: 0.8718 Pre: 0.8846 Recall: 0.8552 F1: 0.8697 Train AUC: 0.9578 Val AUC: 0.9492 Val PRC: 0.9520 Time: 0.24\n",
      "Epoch: 76 Train Loss: 0.2587 Acc: 0.8699 Pre: 0.8780 Recall: 0.8591 F1: 0.8684 Train AUC: 0.9577 Val AUC: 0.9475 Val PRC: 0.9429 Time: 0.23\n",
      "Epoch: 77 Train Loss: 0.2671 Acc: 0.8679 Pre: 0.8357 Recall: 0.9159 F1: 0.8739 Train AUC: 0.9549 Val AUC: 0.9506 Val PRC: 0.9442 Time: 0.23\n",
      "Epoch: 78 Train Loss: 0.2426 Acc: 0.8738 Pre: 0.8835 Recall: 0.8611 F1: 0.8722 Train AUC: 0.9629 Val AUC: 0.9493 Val PRC: 0.9427 Time: 0.24\n",
      "Epoch: 79 Train Loss: 0.2516 Acc: 0.8718 Pre: 0.8815 Recall: 0.8591 F1: 0.8702 Train AUC: 0.9609 Val AUC: 0.9472 Val PRC: 0.9470 Time: 0.23\n",
      "Epoch: 80 Train Loss: 0.2401 Acc: 0.8816 Pre: 0.8794 Recall: 0.8845 F1: 0.8820 Train AUC: 0.9653 Val AUC: 0.9510 Val PRC: 0.9465 Time: 0.23\n",
      "Epoch: 81 Train Loss: 0.2417 Acc: 0.8777 Pre: 0.8907 Recall: 0.8611 F1: 0.8756 Train AUC: 0.9646 Val AUC: 0.9491 Val PRC: 0.9440 Time: 0.23\n",
      "Epoch: 82 Train Loss: 0.2370 Acc: 0.8728 Pre: 0.8802 Recall: 0.8630 F1: 0.8715 Train AUC: 0.9656 Val AUC: 0.9508 Val PRC: 0.9487 Time: 0.23\n",
      "Epoch: 83 Train Loss: 0.2456 Acc: 0.8787 Pre: 0.8630 Recall: 0.9002 F1: 0.8812 Train AUC: 0.9632 Val AUC: 0.9498 Val PRC: 0.9450 Time: 0.23\n",
      "Epoch: 84 Train Loss: 0.2403 Acc: 0.8718 Pre: 0.8585 Recall: 0.8904 F1: 0.8742 Train AUC: 0.9643 Val AUC: 0.9519 Val PRC: 0.9501 Time: 0.24\n",
      "Epoch: 85 Train Loss: 0.2371 Acc: 0.8816 Pre: 0.8931 Recall: 0.8669 F1: 0.8798 Train AUC: 0.9655 Val AUC: 0.9531 Val PRC: 0.9526 Time: 0.23\n",
      "Epoch: 86 Train Loss: 0.2406 Acc: 0.8669 Pre: 0.8415 Recall: 0.9041 F1: 0.8717 Train AUC: 0.9648 Val AUC: 0.9508 Val PRC: 0.9491 Time: 0.23\n",
      "Epoch: 87 Train Loss: 0.2538 Acc: 0.8826 Pre: 0.8871 Recall: 0.8767 F1: 0.8819 Train AUC: 0.9617 Val AUC: 0.9569 Val PRC: 0.9571 Time: 0.23\n",
      "Epoch: 88 Train Loss: 0.2579 Acc: 0.8728 Pre: 0.8772 Recall: 0.8669 F1: 0.8720 Train AUC: 0.9601 Val AUC: 0.9525 Val PRC: 0.9532 Time: 0.24\n",
      "Epoch: 89 Train Loss: 0.2398 Acc: 0.8718 Pre: 0.8612 Recall: 0.8865 F1: 0.8737 Train AUC: 0.9647 Val AUC: 0.9489 Val PRC: 0.9506 Time: 0.24\n",
      "Epoch: 90 Train Loss: 0.2359 Acc: 0.8640 Pre: 0.8432 Recall: 0.8943 F1: 0.8680 Train AUC: 0.9665 Val AUC: 0.9497 Val PRC: 0.9492 Time: 0.23\n",
      "Epoch: 91 Train Loss: 0.2397 Acc: 0.8699 Pre: 0.8857 Recall: 0.8493 F1: 0.8671 Train AUC: 0.9655 Val AUC: 0.9462 Val PRC: 0.9412 Time: 0.24\n",
      "Epoch: 92 Train Loss: 0.2413 Acc: 0.8689 Pre: 0.8325 Recall: 0.9237 F1: 0.8757 Train AUC: 0.9640 Val AUC: 0.9501 Val PRC: 0.9437 Time: 0.23\n",
      "Epoch: 93 Train Loss: 0.2264 Acc: 0.8826 Pre: 0.9081 Recall: 0.8513 F1: 0.8788 Train AUC: 0.9689 Val AUC: 0.9532 Val PRC: 0.9506 Time: 0.23\n",
      "Epoch: 94 Train Loss: 0.2269 Acc: 0.8796 Pre: 0.8477 Recall: 0.9256 F1: 0.8849 Train AUC: 0.9691 Val AUC: 0.9550 Val PRC: 0.9547 Time: 0.23\n",
      "Epoch: 95 Train Loss: 0.2287 Acc: 0.8845 Pre: 0.8816 Recall: 0.8885 F1: 0.8850 Train AUC: 0.9688 Val AUC: 0.9543 Val PRC: 0.9481 Time: 0.23\n",
      "Epoch: 96 Train Loss: 0.2250 Acc: 0.8757 Pre: 0.8288 Recall: 0.9472 F1: 0.8840 Train AUC: 0.9693 Val AUC: 0.9520 Val PRC: 0.9492 Time: 0.23\n",
      "Epoch: 97 Train Loss: 0.2289 Acc: 0.8738 Pre: 0.8716 Recall: 0.8767 F1: 0.8741 Train AUC: 0.9684 Val AUC: 0.9519 Val PRC: 0.9513 Time: 0.24\n",
      "Epoch: 98 Train Loss: 0.2458 Acc: 0.8787 Pre: 0.8878 Recall: 0.8669 F1: 0.8772 Train AUC: 0.9636 Val AUC: 0.9520 Val PRC: 0.9483 Time: 0.23\n",
      "Epoch: 99 Train Loss: 0.2384 Acc: 0.8718 Pre: 0.8626 Recall: 0.8845 F1: 0.8734 Train AUC: 0.9660 Val AUC: 0.9529 Val PRC: 0.9544 Time: 0.23\n",
      "Epoch: 100 Train Loss: 0.2319 Acc: 0.8679 Pre: 0.8264 Recall: 0.9315 F1: 0.8758 Train AUC: 0.9673 Val AUC: 0.9534 Val PRC: 0.9482 Time: 0.23\n",
      "Epoch: 101 Train Loss: 0.2341 Acc: 0.8816 Pre: 0.8854 Recall: 0.8767 F1: 0.8810 Train AUC: 0.9663 Val AUC: 0.9513 Val PRC: 0.9510 Time: 0.23\n",
      "Epoch: 102 Train Loss: 0.2144 Acc: 0.8718 Pre: 0.8242 Recall: 0.9452 F1: 0.8806 Train AUC: 0.9725 Val AUC: 0.9558 Val PRC: 0.9537 Time: 0.23\n",
      "Epoch: 103 Train Loss: 0.2252 Acc: 0.8816 Pre: 0.8764 Recall: 0.8885 F1: 0.8824 Train AUC: 0.9697 Val AUC: 0.9541 Val PRC: 0.9493 Time: 0.23\n",
      "Epoch: 104 Train Loss: 0.2414 Acc: 0.8679 Pre: 0.8219 Recall: 0.9393 F1: 0.8767 Train AUC: 0.9647 Val AUC: 0.9523 Val PRC: 0.9516 Time: 0.24\n",
      "Epoch: 105 Train Loss: 0.2366 Acc: 0.8855 Pre: 0.8760 Recall: 0.8982 F1: 0.8870 Train AUC: 0.9663 Val AUC: 0.9576 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 106 Train Loss: 0.2155 Acc: 0.8708 Pre: 0.8366 Recall: 0.9217 F1: 0.8771 Train AUC: 0.9736 Val AUC: 0.9548 Val PRC: 0.9494 Time: 0.24\n",
      "Epoch: 107 Train Loss: 0.2153 Acc: 0.8875 Pre: 0.8736 Recall: 0.9061 F1: 0.8895 Train AUC: 0.9722 Val AUC: 0.9571 Val PRC: 0.9503 Time: 0.23\n",
      "Epoch: 108 Train Loss: 0.2384 Acc: 0.8679 Pre: 0.8102 Recall: 0.9609 F1: 0.8791 Train AUC: 0.9672 Val AUC: 0.9530 Val PRC: 0.9474 Time: 0.23\n",
      "Epoch: 109 Train Loss: 0.2222 Acc: 0.8816 Pre: 0.8679 Recall: 0.9002 F1: 0.8838 Train AUC: 0.9705 Val AUC: 0.9541 Val PRC: 0.9469 Time: 0.23\n",
      "Epoch: 110 Train Loss: 0.2313 Acc: 0.8806 Pre: 0.8663 Recall: 0.9002 F1: 0.8829 Train AUC: 0.9669 Val AUC: 0.9547 Val PRC: 0.9527 Time: 0.23\n",
      "Epoch: 111 Train Loss: 0.2347 Acc: 0.8796 Pre: 0.8514 Recall: 0.9198 F1: 0.8843 Train AUC: 0.9675 Val AUC: 0.9557 Val PRC: 0.9503 Time: 0.23\n",
      "Epoch: 112 Train Loss: 0.2145 Acc: 0.8826 Pre: 0.8696 Recall: 0.9002 F1: 0.8846 Train AUC: 0.9723 Val AUC: 0.9555 Val PRC: 0.9497 Time: 0.23\n",
      "Epoch: 113 Train Loss: 0.2123 Acc: 0.8845 Pre: 0.8579 Recall: 0.9217 F1: 0.8887 Train AUC: 0.9728 Val AUC: 0.9576 Val PRC: 0.9523 Time: 0.23\n",
      "Epoch: 114 Train Loss: 0.2246 Acc: 0.8777 Pre: 0.8374 Recall: 0.9374 F1: 0.8846 Train AUC: 0.9706 Val AUC: 0.9565 Val PRC: 0.9516 Time: 0.42\n",
      "Epoch: 115 Train Loss: 0.2125 Acc: 0.8777 Pre: 0.8305 Recall: 0.9491 F1: 0.8858 Train AUC: 0.9726 Val AUC: 0.9556 Val PRC: 0.9485 Time: 0.24\n",
      "Epoch: 116 Train Loss: 0.2142 Acc: 0.8865 Pre: 0.8942 Recall: 0.8767 F1: 0.8854 Train AUC: 0.9736 Val AUC: 0.9569 Val PRC: 0.9583 Time: 0.23\n",
      "Epoch: 117 Train Loss: 0.2228 Acc: 0.8836 Pre: 0.8475 Recall: 0.9354 F1: 0.8893 Train AUC: 0.9700 Val AUC: 0.9589 Val PRC: 0.9549 Time: 0.23\n",
      "Epoch: 118 Train Loss: 0.2095 Acc: 0.8875 Pre: 0.8822 Recall: 0.8943 F1: 0.8882 Train AUC: 0.9734 Val AUC: 0.9570 Val PRC: 0.9580 Time: 0.23\n",
      "Epoch: 119 Train Loss: 0.2085 Acc: 0.8865 Pre: 0.8865 Recall: 0.8865 F1: 0.8865 Train AUC: 0.9737 Val AUC: 0.9573 Val PRC: 0.9578 Time: 0.23\n",
      "Epoch: 120 Train Loss: 0.2118 Acc: 0.8904 Pre: 0.8647 Recall: 0.9256 F1: 0.8941 Train AUC: 0.9724 Val AUC: 0.9553 Val PRC: 0.9539 Time: 0.23\n",
      "Epoch: 121 Train Loss: 0.2156 Acc: 0.8816 Pre: 0.8707 Recall: 0.8963 F1: 0.8833 Train AUC: 0.9715 Val AUC: 0.9542 Val PRC: 0.9548 Time: 0.23\n",
      "Epoch: 122 Train Loss: 0.2177 Acc: 0.8757 Pre: 0.8416 Recall: 0.9256 F1: 0.8816 Train AUC: 0.9720 Val AUC: 0.9545 Val PRC: 0.9534 Time: 0.23\n",
      "Epoch: 123 Train Loss: 0.2076 Acc: 0.8953 Pre: 0.9073 Recall: 0.8806 F1: 0.8937 Train AUC: 0.9745 Val AUC: 0.9572 Val PRC: 0.9577 Time: 0.23\n",
      "Epoch: 124 Train Loss: 0.2180 Acc: 0.8836 Pre: 0.8643 Recall: 0.9100 F1: 0.8866 Train AUC: 0.9720 Val AUC: 0.9567 Val PRC: 0.9565 Time: 0.24\n",
      "Epoch: 125 Train Loss: 0.2200 Acc: 0.8855 Pre: 0.8582 Recall: 0.9237 F1: 0.8897 Train AUC: 0.9707 Val AUC: 0.9549 Val PRC: 0.9544 Time: 0.23\n",
      "Epoch: 126 Train Loss: 0.2087 Acc: 0.8865 Pre: 0.8533 Recall: 0.9335 F1: 0.8916 Train AUC: 0.9736 Val AUC: 0.9573 Val PRC: 0.9552 Time: 0.23\n",
      "Epoch: 127 Train Loss: 0.1977 Acc: 0.8924 Pre: 0.8819 Recall: 0.9061 F1: 0.8938 Train AUC: 0.9768 Val AUC: 0.9592 Val PRC: 0.9600 Time: 0.23\n",
      "Epoch: 128 Train Loss: 0.2190 Acc: 0.8963 Pre: 0.8716 Recall: 0.9295 F1: 0.8996 Train AUC: 0.9714 Val AUC: 0.9594 Val PRC: 0.9589 Time: 0.23\n",
      "Epoch: 129 Train Loss: 0.2049 Acc: 0.8855 Pre: 0.8717 Recall: 0.9041 F1: 0.8876 Train AUC: 0.9743 Val AUC: 0.9596 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 130 Train Loss: 0.2120 Acc: 0.8933 Pre: 0.8764 Recall: 0.9159 F1: 0.8957 Train AUC: 0.9725 Val AUC: 0.9588 Val PRC: 0.9547 Time: 0.23\n",
      "Epoch: 131 Train Loss: 0.2039 Acc: 0.8943 Pre: 0.8824 Recall: 0.9100 F1: 0.8960 Train AUC: 0.9749 Val AUC: 0.9594 Val PRC: 0.9604 Time: 0.23\n",
      "Epoch: 132 Train Loss: 0.2021 Acc: 0.8904 Pre: 0.8661 Recall: 0.9237 F1: 0.8939 Train AUC: 0.9752 Val AUC: 0.9589 Val PRC: 0.9559 Time: 0.23\n",
      "Epoch: 133 Train Loss: 0.1985 Acc: 0.8914 Pre: 0.8610 Recall: 0.9335 F1: 0.8958 Train AUC: 0.9757 Val AUC: 0.9588 Val PRC: 0.9581 Time: 0.23\n",
      "Epoch: 134 Train Loss: 0.2123 Acc: 0.8943 Pre: 0.8766 Recall: 0.9178 F1: 0.8967 Train AUC: 0.9722 Val AUC: 0.9610 Val PRC: 0.9615 Time: 0.24\n",
      "Epoch: 135 Train Loss: 0.1978 Acc: 0.9002 Pre: 0.8808 Recall: 0.9256 F1: 0.9027 Train AUC: 0.9763 Val AUC: 0.9630 Val PRC: 0.9621 Time: 0.23\n",
      "Epoch: 136 Train Loss: 0.2076 Acc: 0.8943 Pre: 0.8838 Recall: 0.9080 F1: 0.8958 Train AUC: 0.9743 Val AUC: 0.9616 Val PRC: 0.9615 Time: 0.23\n",
      "Epoch: 137 Train Loss: 0.1896 Acc: 0.8885 Pre: 0.8551 Recall: 0.9354 F1: 0.8935 Train AUC: 0.9781 Val AUC: 0.9610 Val PRC: 0.9622 Time: 0.23\n",
      "Epoch: 138 Train Loss: 0.1938 Acc: 0.8933 Pre: 0.8655 Recall: 0.9315 F1: 0.8973 Train AUC: 0.9771 Val AUC: 0.9600 Val PRC: 0.9566 Time: 0.23\n",
      "Epoch: 139 Train Loss: 0.1940 Acc: 0.9061 Pre: 0.8879 Recall: 0.9295 F1: 0.9082 Train AUC: 0.9768 Val AUC: 0.9595 Val PRC: 0.9548 Time: 0.23\n",
      "Epoch: 140 Train Loss: 0.1966 Acc: 0.8933 Pre: 0.8681 Recall: 0.9276 F1: 0.8969 Train AUC: 0.9761 Val AUC: 0.9597 Val PRC: 0.9588 Time: 0.24\n",
      "Epoch: 141 Train Loss: 0.1987 Acc: 0.8992 Pre: 0.8864 Recall: 0.9159 F1: 0.9009 Train AUC: 0.9759 Val AUC: 0.9599 Val PRC: 0.9581 Time: 0.24\n",
      "Epoch: 142 Train Loss: 0.1943 Acc: 0.9002 Pre: 0.8971 Recall: 0.9041 F1: 0.9006 Train AUC: 0.9765 Val AUC: 0.9632 Val PRC: 0.9628 Time: 0.23\n",
      "Epoch: 143 Train Loss: 0.1920 Acc: 0.8933 Pre: 0.8628 Recall: 0.9354 F1: 0.8977 Train AUC: 0.9779 Val AUC: 0.9618 Val PRC: 0.9624 Time: 0.23\n",
      "Epoch: 144 Train Loss: 0.1907 Acc: 0.8894 Pre: 0.8566 Recall: 0.9354 F1: 0.8943 Train AUC: 0.9775 Val AUC: 0.9607 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 145 Train Loss: 0.1968 Acc: 0.8933 Pre: 0.8821 Recall: 0.9080 F1: 0.8949 Train AUC: 0.9764 Val AUC: 0.9616 Val PRC: 0.9613 Time: 0.23\n",
      "Epoch: 146 Train Loss: 0.1972 Acc: 0.8973 Pre: 0.9060 Recall: 0.8865 F1: 0.8961 Train AUC: 0.9765 Val AUC: 0.9602 Val PRC: 0.9603 Time: 0.23\n",
      "Epoch: 147 Train Loss: 0.1894 Acc: 0.8973 Pre: 0.8889 Recall: 0.9080 F1: 0.8984 Train AUC: 0.9778 Val AUC: 0.9620 Val PRC: 0.9598 Time: 0.23\n",
      "Epoch: 148 Train Loss: 0.1878 Acc: 0.9051 Pre: 0.8950 Recall: 0.9178 F1: 0.9063 Train AUC: 0.9784 Val AUC: 0.9612 Val PRC: 0.9584 Time: 0.24\n",
      "Epoch: 149 Train Loss: 0.1911 Acc: 0.8953 Pre: 0.8686 Recall: 0.9315 F1: 0.8990 Train AUC: 0.9770 Val AUC: 0.9585 Val PRC: 0.9574 Time: 0.23\n",
      "Epoch: 150 Train Loss: 0.2004 Acc: 0.8953 Pre: 0.8811 Recall: 0.9139 F1: 0.8972 Train AUC: 0.9750 Val AUC: 0.9608 Val PRC: 0.9599 Time: 0.24\n",
      "Epoch: 151 Train Loss: 0.1890 Acc: 0.8943 Pre: 0.8554 Recall: 0.9491 F1: 0.8998 Train AUC: 0.9779 Val AUC: 0.9618 Val PRC: 0.9599 Time: 0.23\n",
      "Epoch: 152 Train Loss: 0.1891 Acc: 0.9012 Pre: 0.9100 Recall: 0.8904 F1: 0.9001 Train AUC: 0.9780 Val AUC: 0.9619 Val PRC: 0.9607 Time: 0.23\n",
      "Epoch: 153 Train Loss: 0.1926 Acc: 0.9012 Pre: 0.8825 Recall: 0.9256 F1: 0.9035 Train AUC: 0.9770 Val AUC: 0.9628 Val PRC: 0.9620 Time: 0.24\n",
      "Epoch: 154 Train Loss: 0.1911 Acc: 0.8953 Pre: 0.8700 Recall: 0.9295 F1: 0.8988 Train AUC: 0.9777 Val AUC: 0.9594 Val PRC: 0.9576 Time: 0.23\n",
      "Epoch: 155 Train Loss: 0.1810 Acc: 0.9002 Pre: 0.8780 Recall: 0.9295 F1: 0.9030 Train AUC: 0.9801 Val AUC: 0.9630 Val PRC: 0.9631 Time: 0.23\n",
      "Epoch: 156 Train Loss: 0.1904 Acc: 0.9012 Pre: 0.8883 Recall: 0.9178 F1: 0.9028 Train AUC: 0.9771 Val AUC: 0.9628 Val PRC: 0.9623 Time: 0.24\n",
      "Epoch: 157 Train Loss: 0.1898 Acc: 0.9002 Pre: 0.8808 Recall: 0.9256 F1: 0.9027 Train AUC: 0.9779 Val AUC: 0.9626 Val PRC: 0.9630 Time: 0.24\n",
      "Epoch: 158 Train Loss: 0.1823 Acc: 0.9031 Pre: 0.8902 Recall: 0.9198 F1: 0.9047 Train AUC: 0.9796 Val AUC: 0.9642 Val PRC: 0.9637 Time: 0.24\n",
      "Epoch: 159 Train Loss: 0.1936 Acc: 0.8973 Pre: 0.8830 Recall: 0.9159 F1: 0.8991 Train AUC: 0.9767 Val AUC: 0.9646 Val PRC: 0.9650 Time: 0.24\n",
      "Epoch: 160 Train Loss: 0.1906 Acc: 0.8973 Pre: 0.8638 Recall: 0.9432 F1: 0.9018 Train AUC: 0.9777 Val AUC: 0.9621 Val PRC: 0.9610 Time: 0.24\n",
      "Epoch: 161 Train Loss: 0.1874 Acc: 0.8953 Pre: 0.8507 Recall: 0.9589 F1: 0.9016 Train AUC: 0.9775 Val AUC: 0.9605 Val PRC: 0.9561 Time: 0.23\n",
      "Epoch: 162 Train Loss: 0.1862 Acc: 0.9070 Pre: 0.8985 Recall: 0.9178 F1: 0.9080 Train AUC: 0.9772 Val AUC: 0.9639 Val PRC: 0.9597 Time: 0.24\n",
      "Epoch: 163 Train Loss: 0.1847 Acc: 0.9070 Pre: 0.9177 Recall: 0.8943 F1: 0.9058 Train AUC: 0.9782 Val AUC: 0.9672 Val PRC: 0.9669 Time: 0.23\n",
      "Epoch: 164 Train Loss: 0.1838 Acc: 0.9012 Pre: 0.8825 Recall: 0.9256 F1: 0.9035 Train AUC: 0.9790 Val AUC: 0.9628 Val PRC: 0.9621 Time: 0.23\n",
      "Epoch: 165 Train Loss: 0.1890 Acc: 0.9100 Pre: 0.9084 Recall: 0.9119 F1: 0.9102 Train AUC: 0.9777 Val AUC: 0.9670 Val PRC: 0.9668 Time: 0.24\n",
      "Epoch: 166 Train Loss: 0.1823 Acc: 0.9031 Pre: 0.8858 Recall: 0.9256 F1: 0.9053 Train AUC: 0.9794 Val AUC: 0.9643 Val PRC: 0.9635 Time: 0.23\n",
      "Epoch: 167 Train Loss: 0.1877 Acc: 0.9119 Pre: 0.9040 Recall: 0.9217 F1: 0.9128 Train AUC: 0.9770 Val AUC: 0.9673 Val PRC: 0.9663 Time: 0.24\n",
      "Epoch: 168 Train Loss: 0.1810 Acc: 0.9070 Pre: 0.8925 Recall: 0.9256 F1: 0.9087 Train AUC: 0.9791 Val AUC: 0.9637 Val PRC: 0.9615 Time: 0.23\n",
      "Epoch: 169 Train Loss: 0.1834 Acc: 0.9070 Pre: 0.8881 Recall: 0.9315 F1: 0.9093 Train AUC: 0.9786 Val AUC: 0.9636 Val PRC: 0.9633 Time: 0.23\n",
      "Epoch: 170 Train Loss: 0.1826 Acc: 0.9022 Pre: 0.8771 Recall: 0.9354 F1: 0.9053 Train AUC: 0.9791 Val AUC: 0.9664 Val PRC: 0.9661 Time: 0.23\n",
      "Epoch: 171 Train Loss: 0.1681 Acc: 0.9090 Pre: 0.9050 Recall: 0.9139 F1: 0.9094 Train AUC: 0.9826 Val AUC: 0.9652 Val PRC: 0.9632 Time: 0.23\n",
      "Epoch: 172 Train Loss: 0.1815 Acc: 0.9080 Pre: 0.8987 Recall: 0.9198 F1: 0.9091 Train AUC: 0.9793 Val AUC: 0.9604 Val PRC: 0.9521 Time: 0.24\n",
      "Epoch: 173 Train Loss: 0.1749 Acc: 0.9061 Pre: 0.8908 Recall: 0.9256 F1: 0.9079 Train AUC: 0.9804 Val AUC: 0.9631 Val PRC: 0.9616 Time: 0.23\n",
      "Epoch: 174 Train Loss: 0.1841 Acc: 0.9090 Pre: 0.9050 Recall: 0.9139 F1: 0.9094 Train AUC: 0.9787 Val AUC: 0.9653 Val PRC: 0.9636 Time: 0.24\n",
      "Epoch: 175 Train Loss: 0.1715 Acc: 0.9022 Pre: 0.8689 Recall: 0.9472 F1: 0.9064 Train AUC: 0.9815 Val AUC: 0.9660 Val PRC: 0.9654 Time: 0.23\n",
      "Epoch: 176 Train Loss: 0.1694 Acc: 0.9012 Pre: 0.8622 Recall: 0.9550 F1: 0.9062 Train AUC: 0.9820 Val AUC: 0.9651 Val PRC: 0.9649 Time: 0.23\n",
      "Epoch: 177 Train Loss: 0.1718 Acc: 0.9031 Pre: 0.8946 Recall: 0.9139 F1: 0.9042 Train AUC: 0.9816 Val AUC: 0.9641 Val PRC: 0.9637 Time: 0.23\n",
      "Epoch: 178 Train Loss: 0.1815 Acc: 0.9070 Pre: 0.8895 Recall: 0.9295 F1: 0.9091 Train AUC: 0.9792 Val AUC: 0.9659 Val PRC: 0.9643 Time: 0.23\n",
      "Epoch: 179 Train Loss: 0.1791 Acc: 0.9012 Pre: 0.8687 Recall: 0.9452 F1: 0.9053 Train AUC: 0.9800 Val AUC: 0.9664 Val PRC: 0.9664 Time: 0.23\n",
      "Epoch: 180 Train Loss: 0.1957 Acc: 0.9022 Pre: 0.8703 Recall: 0.9452 F1: 0.9062 Train AUC: 0.9769 Val AUC: 0.9653 Val PRC: 0.9644 Time: 0.23\n",
      "Epoch: 181 Train Loss: 0.1692 Acc: 0.9012 Pre: 0.8868 Recall: 0.9198 F1: 0.9030 Train AUC: 0.9817 Val AUC: 0.9664 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 182 Train Loss: 0.1832 Acc: 0.9022 Pre: 0.8827 Recall: 0.9276 F1: 0.9046 Train AUC: 0.9792 Val AUC: 0.9650 Val PRC: 0.9657 Time: 0.23\n",
      "Epoch: 183 Train Loss: 0.1687 Acc: 0.9051 Pre: 0.8981 Recall: 0.9139 F1: 0.9059 Train AUC: 0.9819 Val AUC: 0.9643 Val PRC: 0.9653 Time: 0.23\n",
      "Epoch: 184 Train Loss: 0.1797 Acc: 0.9041 Pre: 0.8845 Recall: 0.9295 F1: 0.9065 Train AUC: 0.9792 Val AUC: 0.9636 Val PRC: 0.9623 Time: 0.23\n",
      "Epoch: 185 Train Loss: 0.1632 Acc: 0.9168 Pre: 0.9004 Recall: 0.9374 F1: 0.9185 Train AUC: 0.9828 Val AUC: 0.9665 Val PRC: 0.9665 Time: 0.23\n",
      "Epoch: 186 Train Loss: 0.1746 Acc: 0.9090 Pre: 0.9004 Recall: 0.9198 F1: 0.9100 Train AUC: 0.9801 Val AUC: 0.9655 Val PRC: 0.9635 Time: 0.23\n",
      "Epoch: 187 Train Loss: 0.1660 Acc: 0.9061 Pre: 0.8699 Recall: 0.9550 F1: 0.9104 Train AUC: 0.9822 Val AUC: 0.9674 Val PRC: 0.9661 Time: 0.23\n",
      "Epoch: 188 Train Loss: 0.1663 Acc: 0.9168 Pre: 0.9004 Recall: 0.9374 F1: 0.9185 Train AUC: 0.9819 Val AUC: 0.9687 Val PRC: 0.9684 Time: 0.23\n",
      "Epoch: 189 Train Loss: 0.1665 Acc: 0.9100 Pre: 0.8844 Recall: 0.9432 F1: 0.9129 Train AUC: 0.9825 Val AUC: 0.9693 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 190 Train Loss: 0.1699 Acc: 0.9100 Pre: 0.8916 Recall: 0.9335 F1: 0.9120 Train AUC: 0.9812 Val AUC: 0.9659 Val PRC: 0.9644 Time: 0.23\n",
      "Epoch: 191 Train Loss: 0.1623 Acc: 0.9080 Pre: 0.8770 Recall: 0.9491 F1: 0.9117 Train AUC: 0.9833 Val AUC: 0.9643 Val PRC: 0.9618 Time: 0.23\n",
      "Epoch: 192 Train Loss: 0.1793 Acc: 0.9149 Pre: 0.8985 Recall: 0.9354 F1: 0.9166 Train AUC: 0.9796 Val AUC: 0.9670 Val PRC: 0.9662 Time: 0.23\n",
      "Epoch: 193 Train Loss: 0.1616 Acc: 0.9090 Pre: 0.9035 Recall: 0.9159 F1: 0.9096 Train AUC: 0.9837 Val AUC: 0.9677 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 194 Train Loss: 0.1780 Acc: 0.9139 Pre: 0.9091 Recall: 0.9198 F1: 0.9144 Train AUC: 0.9801 Val AUC: 0.9687 Val PRC: 0.9683 Time: 0.23\n",
      "Epoch: 195 Train Loss: 0.1736 Acc: 0.9119 Pre: 0.8766 Recall: 0.9589 F1: 0.9159 Train AUC: 0.9802 Val AUC: 0.9666 Val PRC: 0.9665 Time: 0.23\n",
      "Epoch: 196 Train Loss: 0.1688 Acc: 0.9149 Pre: 0.9061 Recall: 0.9256 F1: 0.9158 Train AUC: 0.9818 Val AUC: 0.9638 Val PRC: 0.9570 Time: 0.23\n",
      "Epoch: 197 Train Loss: 0.1610 Acc: 0.9100 Pre: 0.8887 Recall: 0.9374 F1: 0.9124 Train AUC: 0.9835 Val AUC: 0.9674 Val PRC: 0.9673 Time: 0.23\n",
      "Epoch: 198 Train Loss: 0.1717 Acc: 0.9149 Pre: 0.8970 Recall: 0.9374 F1: 0.9167 Train AUC: 0.9812 Val AUC: 0.9675 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 199 Train Loss: 0.1719 Acc: 0.9129 Pre: 0.9058 Recall: 0.9217 F1: 0.9137 Train AUC: 0.9816 Val AUC: 0.9688 Val PRC: 0.9692 Time: 0.23\n",
      "Epoch: 200 Train Loss: 0.1734 Acc: 0.9168 Pre: 0.8887 Recall: 0.9530 F1: 0.9197 Train AUC: 0.9821 Val AUC: 0.9699 Val PRC: 0.9698 Time: 0.23\n",
      "Epoch: 201 Train Loss: 0.1566 Acc: 0.9149 Pre: 0.9291 Recall: 0.8982 F1: 0.9134 Train AUC: 0.9844 Val AUC: 0.9670 Val PRC: 0.9664 Time: 0.23\n",
      "Epoch: 202 Train Loss: 0.1598 Acc: 0.9159 Pre: 0.9208 Recall: 0.9100 F1: 0.9154 Train AUC: 0.9838 Val AUC: 0.9674 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 203 Train Loss: 0.1617 Acc: 0.9110 Pre: 0.8962 Recall: 0.9295 F1: 0.9126 Train AUC: 0.9833 Val AUC: 0.9667 Val PRC: 0.9664 Time: 0.23\n",
      "Epoch: 204 Train Loss: 0.1698 Acc: 0.9051 Pre: 0.8950 Recall: 0.9178 F1: 0.9063 Train AUC: 0.9817 Val AUC: 0.9667 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 205 Train Loss: 0.1600 Acc: 0.9080 Pre: 0.8941 Recall: 0.9256 F1: 0.9096 Train AUC: 0.9837 Val AUC: 0.9670 Val PRC: 0.9674 Time: 0.23\n",
      "Epoch: 206 Train Loss: 0.1658 Acc: 0.9061 Pre: 0.8922 Recall: 0.9237 F1: 0.9077 Train AUC: 0.9819 Val AUC: 0.9671 Val PRC: 0.9663 Time: 0.23\n",
      "Epoch: 207 Train Loss: 0.1607 Acc: 0.9070 Pre: 0.8939 Recall: 0.9237 F1: 0.9086 Train AUC: 0.9827 Val AUC: 0.9652 Val PRC: 0.9646 Time: 0.23\n",
      "Epoch: 208 Train Loss: 0.1602 Acc: 0.9159 Pre: 0.8913 Recall: 0.9472 F1: 0.9184 Train AUC: 0.9832 Val AUC: 0.9679 Val PRC: 0.9668 Time: 0.23\n",
      "Epoch: 209 Train Loss: 0.1685 Acc: 0.9100 Pre: 0.8901 Recall: 0.9354 F1: 0.9122 Train AUC: 0.9810 Val AUC: 0.9673 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 210 Train Loss: 0.1654 Acc: 0.9061 Pre: 0.9014 Recall: 0.9119 F1: 0.9066 Train AUC: 0.9820 Val AUC: 0.9685 Val PRC: 0.9669 Time: 0.24\n",
      "Epoch: 211 Train Loss: 0.1456 Acc: 0.9139 Pre: 0.9107 Recall: 0.9178 F1: 0.9142 Train AUC: 0.9866 Val AUC: 0.9676 Val PRC: 0.9680 Time: 0.23\n",
      "Epoch: 212 Train Loss: 0.1585 Acc: 0.9129 Pre: 0.8966 Recall: 0.9335 F1: 0.9147 Train AUC: 0.9832 Val AUC: 0.9664 Val PRC: 0.9639 Time: 0.24\n",
      "Epoch: 213 Train Loss: 0.1553 Acc: 0.9100 Pre: 0.9100 Recall: 0.9100 F1: 0.9100 Train AUC: 0.9841 Val AUC: 0.9683 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 214 Train Loss: 0.1616 Acc: 0.9159 Pre: 0.9126 Recall: 0.9198 F1: 0.9162 Train AUC: 0.9827 Val AUC: 0.9696 Val PRC: 0.9699 Time: 0.24\n",
      "Epoch: 215 Train Loss: 0.1555 Acc: 0.9178 Pre: 0.9051 Recall: 0.9335 F1: 0.9191 Train AUC: 0.9836 Val AUC: 0.9689 Val PRC: 0.9682 Time: 0.23\n",
      "Epoch: 216 Train Loss: 0.1490 Acc: 0.9178 Pre: 0.8917 Recall: 0.9511 F1: 0.9205 Train AUC: 0.9852 Val AUC: 0.9682 Val PRC: 0.9657 Time: 0.23\n",
      "Epoch: 217 Train Loss: 0.1533 Acc: 0.9119 Pre: 0.8935 Recall: 0.9354 F1: 0.9140 Train AUC: 0.9847 Val AUC: 0.9669 Val PRC: 0.9642 Time: 0.23\n",
      "Epoch: 218 Train Loss: 0.1597 Acc: 0.9129 Pre: 0.9042 Recall: 0.9237 F1: 0.9138 Train AUC: 0.9829 Val AUC: 0.9691 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 219 Train Loss: 0.1479 Acc: 0.9168 Pre: 0.9160 Recall: 0.9178 F1: 0.9169 Train AUC: 0.9856 Val AUC: 0.9672 Val PRC: 0.9674 Time: 0.24\n",
      "Epoch: 220 Train Loss: 0.1527 Acc: 0.9080 Pre: 0.8927 Recall: 0.9276 F1: 0.9098 Train AUC: 0.9850 Val AUC: 0.9674 Val PRC: 0.9667 Time: 0.23\n",
      "Epoch: 221 Train Loss: 0.1559 Acc: 0.9119 Pre: 0.8994 Recall: 0.9276 F1: 0.9133 Train AUC: 0.9834 Val AUC: 0.9689 Val PRC: 0.9676 Time: 0.23\n",
      "Epoch: 222 Train Loss: 0.1522 Acc: 0.9070 Pre: 0.9031 Recall: 0.9119 F1: 0.9075 Train AUC: 0.9839 Val AUC: 0.9654 Val PRC: 0.9613 Time: 0.24\n",
      "Epoch: 223 Train Loss: 0.1478 Acc: 0.9159 Pre: 0.9063 Recall: 0.9276 F1: 0.9168 Train AUC: 0.9850 Val AUC: 0.9695 Val PRC: 0.9690 Time: 0.23\n",
      "Epoch: 224 Train Loss: 0.1427 Acc: 0.9149 Pre: 0.9206 Recall: 0.9080 F1: 0.9143 Train AUC: 0.9856 Val AUC: 0.9680 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 225 Train Loss: 0.1486 Acc: 0.9207 Pre: 0.8895 Recall: 0.9609 F1: 0.9238 Train AUC: 0.9852 Val AUC: 0.9694 Val PRC: 0.9671 Time: 0.24\n",
      "Epoch: 226 Train Loss: 0.1494 Acc: 0.9207 Pre: 0.9183 Recall: 0.9237 F1: 0.9210 Train AUC: 0.9847 Val AUC: 0.9698 Val PRC: 0.9701 Time: 0.24\n",
      "Epoch: 227 Train Loss: 0.1682 Acc: 0.9237 Pre: 0.9171 Recall: 0.9315 F1: 0.9243 Train AUC: 0.9853 Val AUC: 0.9700 Val PRC: 0.9691 Time: 0.24\n",
      "Epoch: 228 Train Loss: 0.1488 Acc: 0.9198 Pre: 0.9024 Recall: 0.9413 F1: 0.9215 Train AUC: 0.9844 Val AUC: 0.9687 Val PRC: 0.9661 Time: 0.24\n",
      "Epoch: 229 Train Loss: 0.1563 Acc: 0.9129 Pre: 0.9011 Recall: 0.9276 F1: 0.9142 Train AUC: 0.9831 Val AUC: 0.9652 Val PRC: 0.9564 Time: 0.24\n",
      "Epoch: 230 Train Loss: 0.1423 Acc: 0.9129 Pre: 0.8864 Recall: 0.9472 F1: 0.9158 Train AUC: 0.9864 Val AUC: 0.9700 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 231 Train Loss: 0.1549 Acc: 0.9139 Pre: 0.9205 Recall: 0.9061 F1: 0.9132 Train AUC: 0.9843 Val AUC: 0.9691 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 232 Train Loss: 0.1514 Acc: 0.9207 Pre: 0.9216 Recall: 0.9198 F1: 0.9207 Train AUC: 0.9842 Val AUC: 0.9697 Val PRC: 0.9703 Time: 0.23\n",
      "Epoch: 233 Train Loss: 0.1648 Acc: 0.9159 Pre: 0.9242 Recall: 0.9061 F1: 0.9150 Train AUC: 0.9796 Val AUC: 0.9629 Val PRC: 0.9514 Time: 0.23\n",
      "Epoch: 234 Train Loss: 0.1569 Acc: 0.9100 Pre: 0.8945 Recall: 0.9295 F1: 0.9117 Train AUC: 0.9837 Val AUC: 0.9691 Val PRC: 0.9704 Time: 0.24\n",
      "Epoch: 235 Train Loss: 0.1581 Acc: 0.9139 Pre: 0.8998 Recall: 0.9315 F1: 0.9154 Train AUC: 0.9828 Val AUC: 0.9676 Val PRC: 0.9683 Time: 0.24\n",
      "Epoch: 236 Train Loss: 0.1370 Acc: 0.9149 Pre: 0.9077 Recall: 0.9237 F1: 0.9156 Train AUC: 0.9877 Val AUC: 0.9680 Val PRC: 0.9684 Time: 0.23\n",
      "Epoch: 237 Train Loss: 0.1510 Acc: 0.9100 Pre: 0.8858 Recall: 0.9413 F1: 0.9127 Train AUC: 0.9840 Val AUC: 0.9660 Val PRC: 0.9647 Time: 0.24\n",
      "Epoch: 238 Train Loss: 0.1421 Acc: 0.9129 Pre: 0.8966 Recall: 0.9335 F1: 0.9147 Train AUC: 0.9868 Val AUC: 0.9700 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 239 Train Loss: 0.1495 Acc: 0.9110 Pre: 0.9086 Recall: 0.9139 F1: 0.9112 Train AUC: 0.9846 Val AUC: 0.9716 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 240 Train Loss: 0.1448 Acc: 0.9149 Pre: 0.8941 Recall: 0.9413 F1: 0.9171 Train AUC: 0.9863 Val AUC: 0.9696 Val PRC: 0.9700 Time: 0.23\n",
      "Epoch: 241 Train Loss: 0.1436 Acc: 0.9188 Pre: 0.9163 Recall: 0.9217 F1: 0.9190 Train AUC: 0.9861 Val AUC: 0.9706 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 242 Train Loss: 0.1425 Acc: 0.9198 Pre: 0.9149 Recall: 0.9256 F1: 0.9202 Train AUC: 0.9870 Val AUC: 0.9719 Val PRC: 0.9739 Time: 0.23\n",
      "Epoch: 243 Train Loss: 0.1562 Acc: 0.9168 Pre: 0.8989 Recall: 0.9393 F1: 0.9187 Train AUC: 0.9834 Val AUC: 0.9669 Val PRC: 0.9633 Time: 0.24\n",
      "Epoch: 244 Train Loss: 0.1433 Acc: 0.9178 Pre: 0.9067 Recall: 0.9315 F1: 0.9189 Train AUC: 0.9849 Val AUC: 0.9690 Val PRC: 0.9696 Time: 0.24\n",
      "Epoch: 245 Train Loss: 0.1420 Acc: 0.9178 Pre: 0.9067 Recall: 0.9315 F1: 0.9189 Train AUC: 0.9864 Val AUC: 0.9695 Val PRC: 0.9700 Time: 0.24\n",
      "Epoch: 246 Train Loss: 0.1418 Acc: 0.9159 Pre: 0.9328 Recall: 0.8963 F1: 0.9142 Train AUC: 0.9863 Val AUC: 0.9701 Val PRC: 0.9701 Time: 0.24\n",
      "Epoch: 247 Train Loss: 0.1433 Acc: 0.9119 Pre: 0.8994 Recall: 0.9276 F1: 0.9133 Train AUC: 0.9864 Val AUC: 0.9667 Val PRC: 0.9670 Time: 0.42\n",
      "Epoch: 248 Train Loss: 0.1374 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9868 Val AUC: 0.9700 Val PRC: 0.9701 Time: 0.23\n",
      "Epoch: 249 Train Loss: 0.1355 Acc: 0.9168 Pre: 0.9128 Recall: 0.9217 F1: 0.9172 Train AUC: 0.9872 Val AUC: 0.9717 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 250 Train Loss: 0.1297 Acc: 0.9178 Pre: 0.8961 Recall: 0.9452 F1: 0.9200 Train AUC: 0.9886 Val AUC: 0.9732 Val PRC: 0.9742 Time: 0.23\n",
      "Epoch: 251 Train Loss: 0.1366 Acc: 0.9188 Pre: 0.8978 Recall: 0.9452 F1: 0.9209 Train AUC: 0.9868 Val AUC: 0.9689 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 252 Train Loss: 0.1447 Acc: 0.9217 Pre: 0.9058 Recall: 0.9413 F1: 0.9232 Train AUC: 0.9849 Val AUC: 0.9685 Val PRC: 0.9675 Time: 0.23\n",
      "Epoch: 253 Train Loss: 0.1400 Acc: 0.9207 Pre: 0.9151 Recall: 0.9276 F1: 0.9213 Train AUC: 0.9874 Val AUC: 0.9718 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 254 Train Loss: 0.1290 Acc: 0.9100 Pre: 0.8901 Recall: 0.9354 F1: 0.9122 Train AUC: 0.9889 Val AUC: 0.9689 Val PRC: 0.9699 Time: 0.23\n",
      "Epoch: 255 Train Loss: 0.1499 Acc: 0.9168 Pre: 0.9144 Recall: 0.9198 F1: 0.9171 Train AUC: 0.9848 Val AUC: 0.9677 Val PRC: 0.9677 Time: 0.24\n",
      "Epoch: 256 Train Loss: 0.1379 Acc: 0.9129 Pre: 0.9220 Recall: 0.9022 F1: 0.9120 Train AUC: 0.9863 Val AUC: 0.9658 Val PRC: 0.9675 Time: 0.24\n",
      "Epoch: 257 Train Loss: 0.1456 Acc: 0.9188 Pre: 0.9280 Recall: 0.9080 F1: 0.9179 Train AUC: 0.9863 Val AUC: 0.9690 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 258 Train Loss: 0.1284 Acc: 0.9178 Pre: 0.9178 Recall: 0.9178 F1: 0.9178 Train AUC: 0.9888 Val AUC: 0.9697 Val PRC: 0.9698 Time: 0.24\n",
      "Epoch: 259 Train Loss: 0.1426 Acc: 0.9188 Pre: 0.9100 Recall: 0.9295 F1: 0.9197 Train AUC: 0.9859 Val AUC: 0.9690 Val PRC: 0.9679 Time: 0.24\n",
      "Epoch: 260 Train Loss: 0.1404 Acc: 0.9178 Pre: 0.9114 Recall: 0.9256 F1: 0.9184 Train AUC: 0.9857 Val AUC: 0.9721 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 261 Train Loss: 0.1344 Acc: 0.9198 Pre: 0.9299 Recall: 0.9080 F1: 0.9188 Train AUC: 0.9878 Val AUC: 0.9730 Val PRC: 0.9741 Time: 0.23\n",
      "Epoch: 262 Train Loss: 0.1376 Acc: 0.9227 Pre: 0.9186 Recall: 0.9276 F1: 0.9231 Train AUC: 0.9860 Val AUC: 0.9718 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 263 Train Loss: 0.1439 Acc: 0.9168 Pre: 0.9049 Recall: 0.9315 F1: 0.9180 Train AUC: 0.9860 Val AUC: 0.9685 Val PRC: 0.9698 Time: 0.23\n",
      "Epoch: 264 Train Loss: 0.1361 Acc: 0.9178 Pre: 0.9051 Recall: 0.9335 F1: 0.9191 Train AUC: 0.9869 Val AUC: 0.9700 Val PRC: 0.9702 Time: 0.23\n",
      "Epoch: 265 Train Loss: 0.1358 Acc: 0.9168 Pre: 0.9144 Recall: 0.9198 F1: 0.9171 Train AUC: 0.9869 Val AUC: 0.9712 Val PRC: 0.9719 Time: 0.24\n",
      "Epoch: 266 Train Loss: 0.1471 Acc: 0.9168 Pre: 0.8930 Recall: 0.9472 F1: 0.9193 Train AUC: 0.9840 Val AUC: 0.9706 Val PRC: 0.9701 Time: 0.24\n",
      "Epoch: 267 Train Loss: 0.1342 Acc: 0.9168 Pre: 0.8974 Recall: 0.9413 F1: 0.9188 Train AUC: 0.9877 Val AUC: 0.9696 Val PRC: 0.9690 Time: 0.23\n",
      "Epoch: 268 Train Loss: 0.1358 Acc: 0.9227 Pre: 0.9106 Recall: 0.9374 F1: 0.9238 Train AUC: 0.9865 Val AUC: 0.9708 Val PRC: 0.9705 Time: 0.23\n",
      "Epoch: 269 Train Loss: 0.1327 Acc: 0.9207 Pre: 0.9266 Recall: 0.9139 F1: 0.9202 Train AUC: 0.9878 Val AUC: 0.9715 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 270 Train Loss: 0.1326 Acc: 0.9227 Pre: 0.9303 Recall: 0.9139 F1: 0.9220 Train AUC: 0.9882 Val AUC: 0.9717 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 271 Train Loss: 0.1411 Acc: 0.9227 Pre: 0.9219 Recall: 0.9237 F1: 0.9228 Train AUC: 0.9861 Val AUC: 0.9715 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 272 Train Loss: 0.1258 Acc: 0.9227 Pre: 0.9154 Recall: 0.9315 F1: 0.9234 Train AUC: 0.9887 Val AUC: 0.9714 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 273 Train Loss: 0.1234 Acc: 0.9207 Pre: 0.8967 Recall: 0.9511 F1: 0.9231 Train AUC: 0.9897 Val AUC: 0.9720 Val PRC: 0.9729 Time: 0.24\n",
      "Epoch: 274 Train Loss: 0.1140 Acc: 0.9217 Pre: 0.9105 Recall: 0.9354 F1: 0.9228 Train AUC: 0.9920 Val AUC: 0.9715 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 275 Train Loss: 0.1327 Acc: 0.9227 Pre: 0.9235 Recall: 0.9217 F1: 0.9226 Train AUC: 0.9876 Val AUC: 0.9693 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 276 Train Loss: 0.1327 Acc: 0.9198 Pre: 0.9101 Recall: 0.9315 F1: 0.9207 Train AUC: 0.9879 Val AUC: 0.9711 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 277 Train Loss: 0.1353 Acc: 0.9237 Pre: 0.9270 Recall: 0.9198 F1: 0.9234 Train AUC: 0.9862 Val AUC: 0.9723 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 278 Train Loss: 0.1211 Acc: 0.9237 Pre: 0.9204 Recall: 0.9276 F1: 0.9240 Train AUC: 0.9895 Val AUC: 0.9728 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 279 Train Loss: 0.1297 Acc: 0.9178 Pre: 0.9006 Recall: 0.9393 F1: 0.9195 Train AUC: 0.9876 Val AUC: 0.9717 Val PRC: 0.9714 Time: 0.23\n",
      "Epoch: 280 Train Loss: 0.1307 Acc: 0.9159 Pre: 0.9063 Recall: 0.9276 F1: 0.9168 Train AUC: 0.9870 Val AUC: 0.9691 Val PRC: 0.9687 Time: 0.23\n",
      "Epoch: 281 Train Loss: 0.1326 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9870 Val AUC: 0.9721 Val PRC: 0.9730 Time: 0.24\n",
      "Epoch: 282 Train Loss: 0.1225 Acc: 0.9207 Pre: 0.9167 Recall: 0.9256 F1: 0.9211 Train AUC: 0.9896 Val AUC: 0.9697 Val PRC: 0.9718 Time: 0.24\n",
      "Epoch: 283 Train Loss: 0.1212 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9899 Val AUC: 0.9707 Val PRC: 0.9723 Time: 0.24\n",
      "Epoch: 284 Train Loss: 0.1240 Acc: 0.9159 Pre: 0.9225 Recall: 0.9080 F1: 0.9152 Train AUC: 0.9893 Val AUC: 0.9694 Val PRC: 0.9704 Time: 0.24\n",
      "Epoch: 285 Train Loss: 0.1175 Acc: 0.9266 Pre: 0.9291 Recall: 0.9237 F1: 0.9264 Train AUC: 0.9898 Val AUC: 0.9700 Val PRC: 0.9681 Time: 0.23\n",
      "Epoch: 286 Train Loss: 0.1184 Acc: 0.9168 Pre: 0.9294 Recall: 0.9022 F1: 0.9156 Train AUC: 0.9898 Val AUC: 0.9709 Val PRC: 0.9718 Time: 0.23\n",
      "Epoch: 287 Train Loss: 0.1215 Acc: 0.9129 Pre: 0.9187 Recall: 0.9061 F1: 0.9123 Train AUC: 0.9898 Val AUC: 0.9718 Val PRC: 0.9735 Time: 0.23\n",
      "Epoch: 288 Train Loss: 0.1121 Acc: 0.9178 Pre: 0.9036 Recall: 0.9354 F1: 0.9192 Train AUC: 0.9916 Val AUC: 0.9711 Val PRC: 0.9711 Time: 0.23\n",
      "Epoch: 289 Train Loss: 0.1386 Acc: 0.9168 Pre: 0.9176 Recall: 0.9159 F1: 0.9167 Train AUC: 0.9856 Val AUC: 0.9696 Val PRC: 0.9680 Time: 0.23\n",
      "Epoch: 290 Train Loss: 0.1285 Acc: 0.9198 Pre: 0.9009 Recall: 0.9432 F1: 0.9216 Train AUC: 0.9892 Val AUC: 0.9693 Val PRC: 0.9691 Time: 0.23\n",
      "Epoch: 291 Train Loss: 0.1183 Acc: 0.9207 Pre: 0.9317 Recall: 0.9080 F1: 0.9197 Train AUC: 0.9896 Val AUC: 0.9707 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 292 Train Loss: 0.1224 Acc: 0.9198 Pre: 0.9248 Recall: 0.9139 F1: 0.9193 Train AUC: 0.9887 Val AUC: 0.9686 Val PRC: 0.9680 Time: 0.23\n",
      "Epoch: 293 Train Loss: 0.1283 Acc: 0.9168 Pre: 0.8989 Recall: 0.9393 F1: 0.9187 Train AUC: 0.9878 Val AUC: 0.9712 Val PRC: 0.9715 Time: 0.24\n",
      "Epoch: 294 Train Loss: 0.1130 Acc: 0.9266 Pre: 0.9343 Recall: 0.9178 F1: 0.9260 Train AUC: 0.9904 Val AUC: 0.9722 Val PRC: 0.9731 Time: 0.23\n",
      "Epoch: 295 Train Loss: 0.1333 Acc: 0.9188 Pre: 0.9131 Recall: 0.9256 F1: 0.9193 Train AUC: 0.9873 Val AUC: 0.9721 Val PRC: 0.9738 Time: 0.24\n",
      "Epoch: 296 Train Loss: 0.1217 Acc: 0.9247 Pre: 0.9255 Recall: 0.9237 F1: 0.9246 Train AUC: 0.9892 Val AUC: 0.9712 Val PRC: 0.9722 Time: 0.23\n",
      "Epoch: 297 Train Loss: 0.1116 Acc: 0.9178 Pre: 0.9130 Recall: 0.9237 F1: 0.9183 Train AUC: 0.9919 Val AUC: 0.9705 Val PRC: 0.9705 Time: 0.24\n",
      "Epoch: 298 Train Loss: 0.1182 Acc: 0.9295 Pre: 0.9279 Recall: 0.9315 F1: 0.9297 Train AUC: 0.9892 Val AUC: 0.9711 Val PRC: 0.9699 Time: 0.24\n",
      "Epoch: 299 Train Loss: 0.1123 Acc: 0.9198 Pre: 0.9133 Recall: 0.9276 F1: 0.9204 Train AUC: 0.9912 Val AUC: 0.9705 Val PRC: 0.9719 Time: 0.23\n",
      "Epoch: 300 Train Loss: 0.1240 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9889 Val AUC: 0.9685 Val PRC: 0.9685 Time: 0.23\n",
      "Epoch: 301 Train Loss: 0.1128 Acc: 0.9227 Pre: 0.9186 Recall: 0.9276 F1: 0.9231 Train AUC: 0.9907 Val AUC: 0.9717 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 302 Train Loss: 0.1232 Acc: 0.9217 Pre: 0.9043 Recall: 0.9432 F1: 0.9234 Train AUC: 0.9886 Val AUC: 0.9720 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 303 Train Loss: 0.1155 Acc: 0.9256 Pre: 0.9191 Recall: 0.9335 F1: 0.9262 Train AUC: 0.9901 Val AUC: 0.9728 Val PRC: 0.9753 Time: 0.27\n",
      "Epoch: 304 Train Loss: 0.1255 Acc: 0.9178 Pre: 0.9178 Recall: 0.9178 F1: 0.9178 Train AUC: 0.9882 Val AUC: 0.9698 Val PRC: 0.9734 Time: 0.27\n",
      "Epoch: 305 Train Loss: 0.1114 Acc: 0.9168 Pre: 0.8959 Recall: 0.9432 F1: 0.9190 Train AUC: 0.9903 Val AUC: 0.9700 Val PRC: 0.9707 Time: 0.27\n",
      "Epoch: 306 Train Loss: 0.1187 Acc: 0.9295 Pre: 0.9434 Recall: 0.9139 F1: 0.9284 Train AUC: 0.9895 Val AUC: 0.9695 Val PRC: 0.9702 Time: 0.27\n",
      "Epoch: 307 Train Loss: 0.1283 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9876 Val AUC: 0.9719 Val PRC: 0.9740 Time: 0.27\n",
      "Epoch: 308 Train Loss: 0.1286 Acc: 0.9207 Pre: 0.9151 Recall: 0.9276 F1: 0.9213 Train AUC: 0.9882 Val AUC: 0.9711 Val PRC: 0.9743 Time: 0.27\n",
      "Epoch: 309 Train Loss: 0.1306 Acc: 0.9217 Pre: 0.9234 Recall: 0.9198 F1: 0.9216 Train AUC: 0.9885 Val AUC: 0.9701 Val PRC: 0.9723 Time: 0.27\n",
      "Epoch: 310 Train Loss: 0.1188 Acc: 0.9237 Pre: 0.9287 Recall: 0.9178 F1: 0.9232 Train AUC: 0.9887 Val AUC: 0.9690 Val PRC: 0.9668 Time: 0.28\n",
      "Epoch: 311 Train Loss: 0.1313 Acc: 0.9188 Pre: 0.9246 Recall: 0.9119 F1: 0.9182 Train AUC: 0.9853 Val AUC: 0.9664 Val PRC: 0.9642 Time: 0.28\n",
      "Epoch: 312 Train Loss: 0.1143 Acc: 0.9178 Pre: 0.8976 Recall: 0.9432 F1: 0.9198 Train AUC: 0.9912 Val AUC: 0.9712 Val PRC: 0.9723 Time: 0.26\n",
      "Epoch: 313 Train Loss: 0.1130 Acc: 0.9266 Pre: 0.9129 Recall: 0.9432 F1: 0.9278 Train AUC: 0.9900 Val AUC: 0.9733 Val PRC: 0.9752 Time: 0.27\n",
      "Epoch: 314 Train Loss: 0.1264 Acc: 0.9295 Pre: 0.9213 Recall: 0.9393 F1: 0.9302 Train AUC: 0.9888 Val AUC: 0.9752 Val PRC: 0.9769 Time: 0.27\n",
      "Epoch: 315 Train Loss: 0.1180 Acc: 0.9188 Pre: 0.9263 Recall: 0.9100 F1: 0.9181 Train AUC: 0.9905 Val AUC: 0.9725 Val PRC: 0.9742 Time: 0.27\n",
      "Epoch: 316 Train Loss: 0.1221 Acc: 0.9188 Pre: 0.9196 Recall: 0.9178 F1: 0.9187 Train AUC: 0.9896 Val AUC: 0.9701 Val PRC: 0.9707 Time: 0.27\n",
      "Epoch: 317 Train Loss: 0.1148 Acc: 0.9227 Pre: 0.9269 Recall: 0.9178 F1: 0.9223 Train AUC: 0.9908 Val AUC: 0.9728 Val PRC: 0.9745 Time: 0.27\n",
      "Epoch: 318 Train Loss: 0.1414 Acc: 0.9149 Pre: 0.8955 Recall: 0.9393 F1: 0.9169 Train AUC: 0.9847 Val AUC: 0.9688 Val PRC: 0.9679 Time: 0.27\n",
      "Epoch: 319 Train Loss: 0.1142 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9899 Val AUC: 0.9715 Val PRC: 0.9712 Time: 0.27\n",
      "Epoch: 320 Train Loss: 0.1144 Acc: 0.9217 Pre: 0.9152 Recall: 0.9295 F1: 0.9223 Train AUC: 0.9901 Val AUC: 0.9739 Val PRC: 0.9745 Time: 0.27\n",
      "Epoch: 321 Train Loss: 0.1109 Acc: 0.9295 Pre: 0.9213 Recall: 0.9393 F1: 0.9302 Train AUC: 0.9906 Val AUC: 0.9758 Val PRC: 0.9750 Time: 0.27\n",
      "Epoch: 322 Train Loss: 0.1181 Acc: 0.9247 Pre: 0.9157 Recall: 0.9354 F1: 0.9255 Train AUC: 0.9897 Val AUC: 0.9729 Val PRC: 0.9712 Time: 0.27\n",
      "Epoch: 323 Train Loss: 0.1200 Acc: 0.9207 Pre: 0.9266 Recall: 0.9139 F1: 0.9202 Train AUC: 0.9884 Val AUC: 0.9710 Val PRC: 0.9702 Time: 0.27\n",
      "Epoch: 324 Train Loss: 0.1120 Acc: 0.9286 Pre: 0.9228 Recall: 0.9354 F1: 0.9291 Train AUC: 0.9902 Val AUC: 0.9715 Val PRC: 0.9709 Time: 0.27\n",
      "Epoch: 325 Train Loss: 0.1036 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9924 Val AUC: 0.9713 Val PRC: 0.9718 Time: 0.27\n",
      "Epoch: 326 Train Loss: 0.1043 Acc: 0.9168 Pre: 0.9277 Recall: 0.9041 F1: 0.9158 Train AUC: 0.9919 Val AUC: 0.9693 Val PRC: 0.9691 Time: 0.27\n",
      "Epoch: 327 Train Loss: 0.1234 Acc: 0.9207 Pre: 0.9283 Recall: 0.9119 F1: 0.9200 Train AUC: 0.9883 Val AUC: 0.9713 Val PRC: 0.9723 Time: 0.27\n",
      "Epoch: 328 Train Loss: 0.1254 Acc: 0.9188 Pre: 0.8948 Recall: 0.9491 F1: 0.9212 Train AUC: 0.9876 Val AUC: 0.9699 Val PRC: 0.9730 Time: 0.27\n",
      "Epoch: 329 Train Loss: 0.1090 Acc: 0.9217 Pre: 0.9234 Recall: 0.9198 F1: 0.9216 Train AUC: 0.9915 Val AUC: 0.9683 Val PRC: 0.9718 Time: 0.27\n",
      "Epoch: 330 Train Loss: 0.1123 Acc: 0.9207 Pre: 0.9103 Recall: 0.9335 F1: 0.9217 Train AUC: 0.9901 Val AUC: 0.9717 Val PRC: 0.9741 Time: 0.27\n",
      "Epoch: 331 Train Loss: 0.1131 Acc: 0.9207 Pre: 0.9283 Recall: 0.9119 F1: 0.9200 Train AUC: 0.9897 Val AUC: 0.9700 Val PRC: 0.9706 Time: 0.27\n",
      "Epoch: 332 Train Loss: 0.1164 Acc: 0.9198 Pre: 0.9070 Recall: 0.9354 F1: 0.9210 Train AUC: 0.9898 Val AUC: 0.9741 Val PRC: 0.9751 Time: 0.27\n",
      "Epoch: 333 Train Loss: 0.1257 Acc: 0.9247 Pre: 0.9447 Recall: 0.9022 F1: 0.9229 Train AUC: 0.9868 Val AUC: 0.9709 Val PRC: 0.9720 Time: 0.27\n",
      "Epoch: 334 Train Loss: 0.1138 Acc: 0.9227 Pre: 0.9252 Recall: 0.9198 F1: 0.9225 Train AUC: 0.9913 Val AUC: 0.9724 Val PRC: 0.9737 Time: 0.27\n",
      "Epoch: 335 Train Loss: 0.1086 Acc: 0.9207 Pre: 0.9300 Recall: 0.9100 F1: 0.9199 Train AUC: 0.9909 Val AUC: 0.9721 Val PRC: 0.9747 Time: 0.27\n",
      "Epoch: 336 Train Loss: 0.1190 Acc: 0.9198 Pre: 0.9181 Recall: 0.9217 F1: 0.9199 Train AUC: 0.9896 Val AUC: 0.9719 Val PRC: 0.9737 Time: 0.27\n",
      "Epoch: 337 Train Loss: 0.1105 Acc: 0.9237 Pre: 0.9188 Recall: 0.9295 F1: 0.9241 Train AUC: 0.9907 Val AUC: 0.9720 Val PRC: 0.9743 Time: 0.27\n",
      "Epoch: 338 Train Loss: 0.1331 Acc: 0.9256 Pre: 0.9307 Recall: 0.9198 F1: 0.9252 Train AUC: 0.9887 Val AUC: 0.9717 Val PRC: 0.9735 Time: 0.27\n",
      "Epoch: 339 Train Loss: 0.1134 Acc: 0.9188 Pre: 0.8963 Recall: 0.9472 F1: 0.9210 Train AUC: 0.9906 Val AUC: 0.9722 Val PRC: 0.9719 Time: 0.27\n",
      "Epoch: 340 Train Loss: 0.1286 Acc: 0.9247 Pre: 0.9340 Recall: 0.9139 F1: 0.9238 Train AUC: 0.9879 Val AUC: 0.9732 Val PRC: 0.9738 Time: 0.27\n",
      "Epoch: 341 Train Loss: 0.1077 Acc: 0.9168 Pre: 0.9294 Recall: 0.9022 F1: 0.9156 Train AUC: 0.9915 Val AUC: 0.9732 Val PRC: 0.9750 Time: 0.27\n",
      "Epoch: 342 Train Loss: 0.1190 Acc: 0.9227 Pre: 0.9170 Recall: 0.9295 F1: 0.9232 Train AUC: 0.9898 Val AUC: 0.9745 Val PRC: 0.9762 Time: 0.27\n",
      "Epoch: 343 Train Loss: 0.1171 Acc: 0.9227 Pre: 0.9219 Recall: 0.9237 F1: 0.9228 Train AUC: 0.9905 Val AUC: 0.9739 Val PRC: 0.9755 Time: 0.27\n",
      "Epoch: 344 Train Loss: 0.1195 Acc: 0.9227 Pre: 0.9269 Recall: 0.9178 F1: 0.9223 Train AUC: 0.9888 Val AUC: 0.9719 Val PRC: 0.9711 Time: 0.27\n",
      "Epoch: 345 Train Loss: 0.1102 Acc: 0.9256 Pre: 0.9127 Recall: 0.9413 F1: 0.9268 Train AUC: 0.9901 Val AUC: 0.9741 Val PRC: 0.9741 Time: 0.27\n",
      "Epoch: 346 Train Loss: 0.1079 Acc: 0.9188 Pre: 0.9163 Recall: 0.9217 F1: 0.9190 Train AUC: 0.9911 Val AUC: 0.9694 Val PRC: 0.9683 Time: 0.27\n",
      "Epoch: 347 Train Loss: 0.1142 Acc: 0.9217 Pre: 0.9136 Recall: 0.9315 F1: 0.9225 Train AUC: 0.9892 Val AUC: 0.9724 Val PRC: 0.9734 Time: 0.27\n",
      "Epoch: 348 Train Loss: 0.1107 Acc: 0.9119 Pre: 0.8935 Recall: 0.9354 F1: 0.9140 Train AUC: 0.9895 Val AUC: 0.9694 Val PRC: 0.9695 Time: 0.27\n",
      "Epoch: 349 Train Loss: 0.1206 Acc: 0.9217 Pre: 0.9184 Recall: 0.9256 F1: 0.9220 Train AUC: 0.9889 Val AUC: 0.9709 Val PRC: 0.9734 Time: 0.27\n",
      "Epoch: 350 Train Loss: 0.1072 Acc: 0.9227 Pre: 0.9106 Recall: 0.9374 F1: 0.9238 Train AUC: 0.9906 Val AUC: 0.9732 Val PRC: 0.9747 Time: 0.27\n",
      "Epoch: 351 Train Loss: 0.1078 Acc: 0.9266 Pre: 0.9378 Recall: 0.9139 F1: 0.9257 Train AUC: 0.9904 Val AUC: 0.9723 Val PRC: 0.9734 Time: 0.27\n",
      "Epoch: 352 Train Loss: 0.1180 Acc: 0.9217 Pre: 0.9319 Recall: 0.9100 F1: 0.9208 Train AUC: 0.9892 Val AUC: 0.9705 Val PRC: 0.9716 Time: 0.27\n",
      "Epoch: 353 Train Loss: 0.1199 Acc: 0.9227 Pre: 0.9030 Recall: 0.9472 F1: 0.9245 Train AUC: 0.9870 Val AUC: 0.9726 Val PRC: 0.9735 Time: 0.27\n",
      "Epoch: 354 Train Loss: 0.1167 Acc: 0.9256 Pre: 0.9376 Recall: 0.9119 F1: 0.9246 Train AUC: 0.9884 Val AUC: 0.9706 Val PRC: 0.9728 Time: 0.27\n",
      "Epoch: 355 Train Loss: 0.1111 Acc: 0.9256 Pre: 0.9223 Recall: 0.9295 F1: 0.9259 Train AUC: 0.9900 Val AUC: 0.9720 Val PRC: 0.9741 Time: 0.27\n",
      "Epoch: 356 Train Loss: 0.1121 Acc: 0.9266 Pre: 0.9343 Recall: 0.9178 F1: 0.9260 Train AUC: 0.9898 Val AUC: 0.9714 Val PRC: 0.9737 Time: 0.27\n",
      "Epoch: 357 Train Loss: 0.0918 Acc: 0.9207 Pre: 0.9232 Recall: 0.9178 F1: 0.9205 Train AUC: 0.9935 Val AUC: 0.9727 Val PRC: 0.9743 Time: 0.27\n",
      "Epoch: 358 Train Loss: 0.1013 Acc: 0.9237 Pre: 0.9374 Recall: 0.9080 F1: 0.9225 Train AUC: 0.9914 Val AUC: 0.9703 Val PRC: 0.9722 Time: 0.27\n",
      "Epoch: 359 Train Loss: 0.0987 Acc: 0.9207 Pre: 0.9249 Recall: 0.9159 F1: 0.9204 Train AUC: 0.9922 Val AUC: 0.9694 Val PRC: 0.9709 Time: 0.27\n",
      "Epoch: 360 Train Loss: 0.1168 Acc: 0.9168 Pre: 0.9096 Recall: 0.9256 F1: 0.9176 Train AUC: 0.9883 Val AUC: 0.9692 Val PRC: 0.9717 Time: 0.27\n",
      "Epoch: 361 Train Loss: 0.1072 Acc: 0.9247 Pre: 0.9375 Recall: 0.9100 F1: 0.9235 Train AUC: 0.9903 Val AUC: 0.9702 Val PRC: 0.9720 Time: 0.27\n",
      "Epoch: 362 Train Loss: 0.1057 Acc: 0.9178 Pre: 0.9366 Recall: 0.8963 F1: 0.9160 Train AUC: 0.9910 Val AUC: 0.9718 Val PRC: 0.9743 Time: 0.27\n",
      "Epoch: 363 Train Loss: 0.0912 Acc: 0.9227 Pre: 0.9355 Recall: 0.9080 F1: 0.9215 Train AUC: 0.9926 Val AUC: 0.9719 Val PRC: 0.9738 Time: 0.27\n",
      "Epoch: 364 Train Loss: 0.0932 Acc: 0.9276 Pre: 0.9379 Recall: 0.9159 F1: 0.9267 Train AUC: 0.9921 Val AUC: 0.9723 Val PRC: 0.9727 Time: 0.27\n",
      "Epoch: 365 Train Loss: 0.1121 Acc: 0.9295 Pre: 0.9399 Recall: 0.9178 F1: 0.9287 Train AUC: 0.9886 Val AUC: 0.9719 Val PRC: 0.9736 Time: 0.27\n",
      "Epoch: 366 Train Loss: 0.1013 Acc: 0.9295 Pre: 0.9489 Recall: 0.9080 F1: 0.9280 Train AUC: 0.9901 Val AUC: 0.9709 Val PRC: 0.9688 Time: 0.27\n",
      "Epoch: 367 Train Loss: 0.1010 Acc: 0.9256 Pre: 0.9307 Recall: 0.9198 F1: 0.9252 Train AUC: 0.9916 Val AUC: 0.9721 Val PRC: 0.9747 Time: 0.27\n",
      "Epoch: 368 Train Loss: 0.1076 Acc: 0.9247 Pre: 0.9306 Recall: 0.9178 F1: 0.9241 Train AUC: 0.9901 Val AUC: 0.9699 Val PRC: 0.9713 Time: 0.27\n",
      "Epoch: 369 Train Loss: 0.1003 Acc: 0.9217 Pre: 0.9201 Recall: 0.9237 F1: 0.9219 Train AUC: 0.9917 Val AUC: 0.9722 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 370 Train Loss: 0.0973 Acc: 0.9237 Pre: 0.9220 Recall: 0.9256 F1: 0.9238 Train AUC: 0.9919 Val AUC: 0.9729 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 371 Train Loss: 0.0916 Acc: 0.9227 Pre: 0.9303 Recall: 0.9139 F1: 0.9220 Train AUC: 0.9932 Val AUC: 0.9737 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 372 Train Loss: 0.0965 Acc: 0.9247 Pre: 0.9323 Recall: 0.9159 F1: 0.9240 Train AUC: 0.9925 Val AUC: 0.9746 Val PRC: 0.9771 Time: 0.23\n",
      "Epoch: 373 Train Loss: 0.1032 Acc: 0.9266 Pre: 0.9208 Recall: 0.9335 F1: 0.9271 Train AUC: 0.9910 Val AUC: 0.9744 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 374 Train Loss: 0.1103 Acc: 0.9286 Pre: 0.9451 Recall: 0.9100 F1: 0.9272 Train AUC: 0.9899 Val AUC: 0.9754 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 375 Train Loss: 0.0984 Acc: 0.9227 Pre: 0.9060 Recall: 0.9432 F1: 0.9243 Train AUC: 0.9920 Val AUC: 0.9740 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 376 Train Loss: 0.0973 Acc: 0.9227 Pre: 0.9286 Recall: 0.9159 F1: 0.9222 Train AUC: 0.9919 Val AUC: 0.9723 Val PRC: 0.9732 Time: 0.23\n",
      "Epoch: 377 Train Loss: 0.0919 Acc: 0.9227 Pre: 0.9170 Recall: 0.9295 F1: 0.9232 Train AUC: 0.9928 Val AUC: 0.9701 Val PRC: 0.9677 Time: 0.23\n",
      "Epoch: 378 Train Loss: 0.0821 Acc: 0.9159 Pre: 0.9328 Recall: 0.8963 F1: 0.9142 Train AUC: 0.9934 Val AUC: 0.9698 Val PRC: 0.9713 Time: 0.24\n",
      "Epoch: 379 Train Loss: 0.1049 Acc: 0.9217 Pre: 0.9028 Recall: 0.9452 F1: 0.9235 Train AUC: 0.9900 Val AUC: 0.9708 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 380 Train Loss: 0.1120 Acc: 0.9188 Pre: 0.8905 Recall: 0.9550 F1: 0.9216 Train AUC: 0.9895 Val AUC: 0.9742 Val PRC: 0.9758 Time: 0.41\n",
      "Epoch: 381 Train Loss: 0.0958 Acc: 0.9266 Pre: 0.9467 Recall: 0.9041 F1: 0.9249 Train AUC: 0.9927 Val AUC: 0.9728 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 382 Train Loss: 0.1054 Acc: 0.9198 Pre: 0.9231 Recall: 0.9159 F1: 0.9194 Train AUC: 0.9913 Val AUC: 0.9724 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 383 Train Loss: 0.0870 Acc: 0.9237 Pre: 0.9409 Recall: 0.9041 F1: 0.9222 Train AUC: 0.9940 Val AUC: 0.9738 Val PRC: 0.9757 Time: 0.23\n",
      "Epoch: 384 Train Loss: 0.0875 Acc: 0.9198 Pre: 0.9086 Recall: 0.9335 F1: 0.9208 Train AUC: 0.9934 Val AUC: 0.9719 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 385 Train Loss: 0.0983 Acc: 0.9217 Pre: 0.9301 Recall: 0.9119 F1: 0.9209 Train AUC: 0.9914 Val AUC: 0.9715 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 386 Train Loss: 0.1025 Acc: 0.9286 Pre: 0.9294 Recall: 0.9276 F1: 0.9285 Train AUC: 0.9910 Val AUC: 0.9720 Val PRC: 0.9736 Time: 0.24\n",
      "Epoch: 387 Train Loss: 0.1058 Acc: 0.9217 Pre: 0.9319 Recall: 0.9100 F1: 0.9208 Train AUC: 0.9892 Val AUC: 0.9702 Val PRC: 0.9666 Time: 0.24\n",
      "Epoch: 388 Train Loss: 0.1003 Acc: 0.9217 Pre: 0.9250 Recall: 0.9178 F1: 0.9214 Train AUC: 0.9917 Val AUC: 0.9722 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 389 Train Loss: 0.1058 Acc: 0.9227 Pre: 0.9303 Recall: 0.9139 F1: 0.9220 Train AUC: 0.9905 Val AUC: 0.9731 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 390 Train Loss: 0.0954 Acc: 0.9227 Pre: 0.9390 Recall: 0.9041 F1: 0.9212 Train AUC: 0.9923 Val AUC: 0.9746 Val PRC: 0.9768 Time: 0.23\n",
      "Epoch: 391 Train Loss: 0.1017 Acc: 0.9247 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9917 Val AUC: 0.9732 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 392 Train Loss: 0.1090 Acc: 0.9237 Pre: 0.9204 Recall: 0.9276 F1: 0.9240 Train AUC: 0.9903 Val AUC: 0.9719 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 393 Train Loss: 0.1076 Acc: 0.9256 Pre: 0.9207 Recall: 0.9315 F1: 0.9261 Train AUC: 0.9913 Val AUC: 0.9729 Val PRC: 0.9736 Time: 0.23\n",
      "Epoch: 394 Train Loss: 0.1177 Acc: 0.9237 Pre: 0.9032 Recall: 0.9491 F1: 0.9256 Train AUC: 0.9870 Val AUC: 0.9695 Val PRC: 0.9658 Time: 0.23\n",
      "Epoch: 395 Train Loss: 0.0907 Acc: 0.9247 Pre: 0.9289 Recall: 0.9198 F1: 0.9243 Train AUC: 0.9926 Val AUC: 0.9734 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 396 Train Loss: 0.1011 Acc: 0.9237 Pre: 0.9047 Recall: 0.9472 F1: 0.9254 Train AUC: 0.9914 Val AUC: 0.9705 Val PRC: 0.9740 Time: 0.24\n",
      "Epoch: 397 Train Loss: 0.0958 Acc: 0.9119 Pre: 0.9056 Recall: 0.9198 F1: 0.9126 Train AUC: 0.9918 Val AUC: 0.9695 Val PRC: 0.9725 Time: 0.24\n",
      "Epoch: 398 Train Loss: 0.0937 Acc: 0.9227 Pre: 0.9303 Recall: 0.9139 F1: 0.9220 Train AUC: 0.9918 Val AUC: 0.9712 Val PRC: 0.9738 Time: 0.23\n",
      "Epoch: 399 Train Loss: 0.0867 Acc: 0.9207 Pre: 0.9283 Recall: 0.9119 F1: 0.9200 Train AUC: 0.9939 Val AUC: 0.9713 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 400 Train Loss: 0.0886 Acc: 0.9276 Pre: 0.9379 Recall: 0.9159 F1: 0.9267 Train AUC: 0.9932 Val AUC: 0.9723 Val PRC: 0.9758 Time: 0.24\n",
      "Epoch: 401 Train Loss: 0.0949 Acc: 0.9247 Pre: 0.9429 Recall: 0.9041 F1: 0.9231 Train AUC: 0.9927 Val AUC: 0.9705 Val PRC: 0.9732 Time: 0.24\n",
      "Epoch: 402 Train Loss: 0.0988 Acc: 0.9168 Pre: 0.9294 Recall: 0.9022 F1: 0.9156 Train AUC: 0.9923 Val AUC: 0.9675 Val PRC: 0.9696 Time: 0.23\n",
      "Epoch: 403 Train Loss: 0.1010 Acc: 0.9207 Pre: 0.9087 Recall: 0.9354 F1: 0.9219 Train AUC: 0.9921 Val AUC: 0.9712 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 404 Train Loss: 0.1055 Acc: 0.9276 Pre: 0.9361 Recall: 0.9178 F1: 0.9269 Train AUC: 0.9910 Val AUC: 0.9722 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 405 Train Loss: 0.1034 Acc: 0.9198 Pre: 0.9351 Recall: 0.9022 F1: 0.9183 Train AUC: 0.9912 Val AUC: 0.9730 Val PRC: 0.9768 Time: 0.24\n",
      "Epoch: 406 Train Loss: 0.0947 Acc: 0.9178 Pre: 0.9051 Recall: 0.9335 F1: 0.9191 Train AUC: 0.9910 Val AUC: 0.9736 Val PRC: 0.9763 Time: 0.24\n",
      "Epoch: 407 Train Loss: 0.1132 Acc: 0.9139 Pre: 0.9107 Recall: 0.9178 F1: 0.9142 Train AUC: 0.9886 Val AUC: 0.9710 Val PRC: 0.9739 Time: 0.24\n",
      "Epoch: 408 Train Loss: 0.0937 Acc: 0.9295 Pre: 0.9312 Recall: 0.9276 F1: 0.9294 Train AUC: 0.9924 Val AUC: 0.9702 Val PRC: 0.9730 Time: 0.23\n",
      "Epoch: 409 Train Loss: 0.1044 Acc: 0.9159 Pre: 0.9094 Recall: 0.9237 F1: 0.9165 Train AUC: 0.9908 Val AUC: 0.9682 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 410 Train Loss: 0.0950 Acc: 0.9227 Pre: 0.9122 Recall: 0.9354 F1: 0.9237 Train AUC: 0.9915 Val AUC: 0.9690 Val PRC: 0.9704 Time: 0.23\n",
      "Epoch: 411 Train Loss: 0.1009 Acc: 0.9256 Pre: 0.9096 Recall: 0.9452 F1: 0.9271 Train AUC: 0.9911 Val AUC: 0.9691 Val PRC: 0.9698 Time: 0.23\n",
      "Epoch: 412 Train Loss: 0.0880 Acc: 0.9344 Pre: 0.9336 Recall: 0.9354 F1: 0.9345 Train AUC: 0.9934 Val AUC: 0.9729 Val PRC: 0.9756 Time: 0.24\n",
      "Epoch: 413 Train Loss: 0.1010 Acc: 0.9276 Pre: 0.9414 Recall: 0.9119 F1: 0.9264 Train AUC: 0.9911 Val AUC: 0.9701 Val PRC: 0.9748 Time: 0.24\n",
      "Epoch: 414 Train Loss: 0.0908 Acc: 0.9227 Pre: 0.9557 Recall: 0.8865 F1: 0.9198 Train AUC: 0.9929 Val AUC: 0.9680 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 415 Train Loss: 0.0986 Acc: 0.9237 Pre: 0.9374 Recall: 0.9080 F1: 0.9225 Train AUC: 0.9915 Val AUC: 0.9698 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 416 Train Loss: 0.1037 Acc: 0.9247 Pre: 0.9375 Recall: 0.9100 F1: 0.9235 Train AUC: 0.9908 Val AUC: 0.9697 Val PRC: 0.9727 Time: 0.24\n",
      "Epoch: 417 Train Loss: 0.0911 Acc: 0.9198 Pre: 0.9264 Recall: 0.9119 F1: 0.9191 Train AUC: 0.9932 Val AUC: 0.9671 Val PRC: 0.9702 Time: 0.24\n",
      "Epoch: 418 Train Loss: 0.0972 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9915 Val AUC: 0.9681 Val PRC: 0.9731 Time: 0.24\n",
      "Epoch: 419 Train Loss: 0.0947 Acc: 0.9276 Pre: 0.9162 Recall: 0.9413 F1: 0.9286 Train AUC: 0.9916 Val AUC: 0.9710 Val PRC: 0.9723 Time: 0.24\n",
      "Epoch: 420 Train Loss: 0.1196 Acc: 0.9149 Pre: 0.8985 Recall: 0.9354 F1: 0.9166 Train AUC: 0.9869 Val AUC: 0.9735 Val PRC: 0.9748 Time: 0.23\n",
      "Epoch: 421 Train Loss: 0.0959 Acc: 0.9315 Pre: 0.9455 Recall: 0.9159 F1: 0.9304 Train AUC: 0.9915 Val AUC: 0.9709 Val PRC: 0.9626 Time: 0.23\n",
      "Epoch: 422 Train Loss: 0.0957 Acc: 0.9217 Pre: 0.9250 Recall: 0.9178 F1: 0.9214 Train AUC: 0.9923 Val AUC: 0.9705 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 423 Train Loss: 0.0936 Acc: 0.9286 Pre: 0.9415 Recall: 0.9139 F1: 0.9275 Train AUC: 0.9928 Val AUC: 0.9695 Val PRC: 0.9720 Time: 0.23\n",
      "Epoch: 424 Train Loss: 0.1225 Acc: 0.9237 Pre: 0.9304 Recall: 0.9159 F1: 0.9231 Train AUC: 0.9913 Val AUC: 0.9723 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 425 Train Loss: 0.1159 Acc: 0.9266 Pre: 0.9241 Recall: 0.9295 F1: 0.9268 Train AUC: 0.9883 Val AUC: 0.9719 Val PRC: 0.9745 Time: 0.23\n",
      "Epoch: 426 Train Loss: 0.0954 Acc: 0.9305 Pre: 0.9472 Recall: 0.9119 F1: 0.9292 Train AUC: 0.9919 Val AUC: 0.9741 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 427 Train Loss: 0.0986 Acc: 0.9237 Pre: 0.9237 Recall: 0.9237 F1: 0.9237 Train AUC: 0.9916 Val AUC: 0.9718 Val PRC: 0.9763 Time: 0.23\n",
      "Epoch: 428 Train Loss: 0.0927 Acc: 0.9286 Pre: 0.9433 Recall: 0.9119 F1: 0.9274 Train AUC: 0.9924 Val AUC: 0.9732 Val PRC: 0.9761 Time: 0.23\n",
      "Epoch: 429 Train Loss: 0.0899 Acc: 0.9178 Pre: 0.9146 Recall: 0.9217 F1: 0.9181 Train AUC: 0.9932 Val AUC: 0.9709 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 430 Train Loss: 0.0868 Acc: 0.9139 Pre: 0.9029 Recall: 0.9276 F1: 0.9151 Train AUC: 0.9940 Val AUC: 0.9708 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 431 Train Loss: 0.1021 Acc: 0.9207 Pre: 0.9352 Recall: 0.9041 F1: 0.9194 Train AUC: 0.9911 Val AUC: 0.9712 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 432 Train Loss: 0.0958 Acc: 0.9198 Pre: 0.9369 Recall: 0.9002 F1: 0.9182 Train AUC: 0.9915 Val AUC: 0.9699 Val PRC: 0.9713 Time: 0.23\n",
      "Epoch: 433 Train Loss: 0.1001 Acc: 0.9305 Pre: 0.9583 Recall: 0.9002 F1: 0.9284 Train AUC: 0.9915 Val AUC: 0.9730 Val PRC: 0.9757 Time: 0.24\n",
      "Epoch: 434 Train Loss: 0.0933 Acc: 0.9266 Pre: 0.9360 Recall: 0.9159 F1: 0.9258 Train AUC: 0.9914 Val AUC: 0.9716 Val PRC: 0.9722 Time: 0.24\n",
      "Epoch: 435 Train Loss: 0.0880 Acc: 0.9256 Pre: 0.9159 Recall: 0.9374 F1: 0.9265 Train AUC: 0.9935 Val AUC: 0.9735 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 436 Train Loss: 0.0976 Acc: 0.9247 Pre: 0.9483 Recall: 0.8982 F1: 0.9226 Train AUC: 0.9923 Val AUC: 0.9714 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 437 Train Loss: 0.0876 Acc: 0.9227 Pre: 0.9337 Recall: 0.9100 F1: 0.9217 Train AUC: 0.9929 Val AUC: 0.9728 Val PRC: 0.9756 Time: 0.23\n",
      "Epoch: 438 Train Loss: 0.0842 Acc: 0.9227 Pre: 0.9269 Recall: 0.9178 F1: 0.9223 Train AUC: 0.9936 Val AUC: 0.9721 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 439 Train Loss: 0.0944 Acc: 0.9227 Pre: 0.9252 Recall: 0.9198 F1: 0.9225 Train AUC: 0.9925 Val AUC: 0.9707 Val PRC: 0.9749 Time: 0.23\n",
      "Epoch: 440 Train Loss: 0.0851 Acc: 0.9168 Pre: 0.9456 Recall: 0.8845 F1: 0.9141 Train AUC: 0.9941 Val AUC: 0.9690 Val PRC: 0.9741 Time: 0.24\n",
      "Epoch: 441 Train Loss: 0.0850 Acc: 0.9237 Pre: 0.9304 Recall: 0.9159 F1: 0.9231 Train AUC: 0.9940 Val AUC: 0.9705 Val PRC: 0.9750 Time: 0.24\n",
      "Epoch: 442 Train Loss: 0.0896 Acc: 0.9159 Pre: 0.9094 Recall: 0.9237 F1: 0.9165 Train AUC: 0.9935 Val AUC: 0.9681 Val PRC: 0.9725 Time: 0.23\n",
      "Epoch: 443 Train Loss: 0.0942 Acc: 0.9207 Pre: 0.9370 Recall: 0.9022 F1: 0.9192 Train AUC: 0.9927 Val AUC: 0.9710 Val PRC: 0.9735 Time: 0.24\n",
      "Epoch: 444 Train Loss: 0.1119 Acc: 0.9207 Pre: 0.9216 Recall: 0.9198 F1: 0.9207 Train AUC: 0.9925 Val AUC: 0.9710 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 445 Train Loss: 0.0901 Acc: 0.9266 Pre: 0.9523 Recall: 0.8982 F1: 0.9245 Train AUC: 0.9924 Val AUC: 0.9723 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 446 Train Loss: 0.0817 Acc: 0.9227 Pre: 0.9060 Recall: 0.9432 F1: 0.9243 Train AUC: 0.9934 Val AUC: 0.9747 Val PRC: 0.9766 Time: 0.23\n",
      "Epoch: 447 Train Loss: 0.0996 Acc: 0.9295 Pre: 0.9347 Recall: 0.9237 F1: 0.9291 Train AUC: 0.9907 Val AUC: 0.9722 Val PRC: 0.9723 Time: 0.23\n",
      "Epoch: 448 Train Loss: 0.0822 Acc: 0.9286 Pre: 0.9525 Recall: 0.9022 F1: 0.9266 Train AUC: 0.9936 Val AUC: 0.9764 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 449 Train Loss: 0.0909 Acc: 0.9305 Pre: 0.9331 Recall: 0.9276 F1: 0.9303 Train AUC: 0.9924 Val AUC: 0.9760 Val PRC: 0.9788 Time: 0.23\n",
      "Epoch: 450 Train Loss: 0.0928 Acc: 0.9207 Pre: 0.9249 Recall: 0.9159 F1: 0.9204 Train AUC: 0.9923 Val AUC: 0.9747 Val PRC: 0.9769 Time: 0.23\n",
      "Epoch: 451 Train Loss: 0.0923 Acc: 0.9168 Pre: 0.9019 Recall: 0.9354 F1: 0.9183 Train AUC: 0.9916 Val AUC: 0.9715 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 452 Train Loss: 0.0954 Acc: 0.9198 Pre: 0.9133 Recall: 0.9276 F1: 0.9204 Train AUC: 0.9914 Val AUC: 0.9715 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 453 Train Loss: 0.0867 Acc: 0.9256 Pre: 0.9143 Recall: 0.9393 F1: 0.9266 Train AUC: 0.9918 Val AUC: 0.9742 Val PRC: 0.9765 Time: 0.24\n",
      "Epoch: 454 Train Loss: 0.0940 Acc: 0.9247 Pre: 0.9205 Recall: 0.9295 F1: 0.9250 Train AUC: 0.9909 Val AUC: 0.9729 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 455 Train Loss: 0.0816 Acc: 0.9227 Pre: 0.9269 Recall: 0.9178 F1: 0.9223 Train AUC: 0.9940 Val AUC: 0.9699 Val PRC: 0.9706 Time: 0.23\n",
      "Epoch: 456 Train Loss: 0.0870 Acc: 0.9217 Pre: 0.9518 Recall: 0.8885 F1: 0.9190 Train AUC: 0.9937 Val AUC: 0.9719 Val PRC: 0.9751 Time: 0.23\n",
      "Epoch: 457 Train Loss: 0.0929 Acc: 0.9256 Pre: 0.9412 Recall: 0.9080 F1: 0.9243 Train AUC: 0.9929 Val AUC: 0.9710 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 458 Train Loss: 0.0813 Acc: 0.9168 Pre: 0.9591 Recall: 0.8708 F1: 0.9128 Train AUC: 0.9938 Val AUC: 0.9693 Val PRC: 0.9734 Time: 0.23\n",
      "Epoch: 459 Train Loss: 0.0773 Acc: 0.9129 Pre: 0.8809 Recall: 0.9550 F1: 0.9164 Train AUC: 0.9945 Val AUC: 0.9743 Val PRC: 0.9762 Time: 0.23\n",
      "Epoch: 460 Train Loss: 0.0820 Acc: 0.9276 Pre: 0.9276 Recall: 0.9276 F1: 0.9276 Train AUC: 0.9945 Val AUC: 0.9722 Val PRC: 0.9750 Time: 0.23\n",
      "Epoch: 461 Train Loss: 0.0969 Acc: 0.9198 Pre: 0.9214 Recall: 0.9178 F1: 0.9196 Train AUC: 0.9918 Val AUC: 0.9724 Val PRC: 0.9737 Time: 0.24\n",
      "Epoch: 462 Train Loss: 0.0872 Acc: 0.9227 Pre: 0.9481 Recall: 0.8943 F1: 0.9204 Train AUC: 0.9941 Val AUC: 0.9693 Val PRC: 0.9724 Time: 0.24\n",
      "Epoch: 463 Train Loss: 0.0766 Acc: 0.9188 Pre: 0.9315 Recall: 0.9041 F1: 0.9176 Train AUC: 0.9944 Val AUC: 0.9733 Val PRC: 0.9764 Time: 0.23\n",
      "Epoch: 464 Train Loss: 0.0771 Acc: 0.9247 Pre: 0.9393 Recall: 0.9080 F1: 0.9234 Train AUC: 0.9946 Val AUC: 0.9737 Val PRC: 0.9747 Time: 0.23\n",
      "Epoch: 465 Train Loss: 0.1093 Acc: 0.9159 Pre: 0.9048 Recall: 0.9295 F1: 0.9170 Train AUC: 0.9897 Val AUC: 0.9700 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 466 Train Loss: 0.0761 Acc: 0.9188 Pre: 0.9385 Recall: 0.8963 F1: 0.9169 Train AUC: 0.9942 Val AUC: 0.9719 Val PRC: 0.9744 Time: 0.23\n",
      "Epoch: 467 Train Loss: 0.0734 Acc: 0.9217 Pre: 0.9480 Recall: 0.8924 F1: 0.9194 Train AUC: 0.9946 Val AUC: 0.9734 Val PRC: 0.9765 Time: 0.23\n",
      "Epoch: 468 Train Loss: 0.0814 Acc: 0.9090 Pre: 0.9248 Recall: 0.8904 F1: 0.9073 Train AUC: 0.9935 Val AUC: 0.9675 Val PRC: 0.9721 Time: 0.23\n",
      "Epoch: 469 Train Loss: 0.0859 Acc: 0.9227 Pre: 0.9286 Recall: 0.9159 F1: 0.9222 Train AUC: 0.9928 Val AUC: 0.9748 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 470 Train Loss: 0.0809 Acc: 0.9168 Pre: 0.9049 Recall: 0.9315 F1: 0.9180 Train AUC: 0.9937 Val AUC: 0.9712 Val PRC: 0.9733 Time: 0.23\n",
      "Epoch: 471 Train Loss: 0.0978 Acc: 0.9207 Pre: 0.9352 Recall: 0.9041 F1: 0.9194 Train AUC: 0.9914 Val AUC: 0.9732 Val PRC: 0.9756 Time: 0.24\n",
      "Epoch: 472 Train Loss: 0.0910 Acc: 0.9247 Pre: 0.9141 Recall: 0.9374 F1: 0.9256 Train AUC: 0.9924 Val AUC: 0.9745 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 473 Train Loss: 0.0850 Acc: 0.9305 Pre: 0.9622 Recall: 0.8963 F1: 0.9281 Train AUC: 0.9933 Val AUC: 0.9736 Val PRC: 0.9779 Time: 0.24\n",
      "Epoch: 474 Train Loss: 0.0828 Acc: 0.9305 Pre: 0.9490 Recall: 0.9100 F1: 0.9291 Train AUC: 0.9937 Val AUC: 0.9722 Val PRC: 0.9772 Time: 0.24\n",
      "Epoch: 475 Train Loss: 0.0812 Acc: 0.9247 Pre: 0.9306 Recall: 0.9178 F1: 0.9241 Train AUC: 0.9934 Val AUC: 0.9727 Val PRC: 0.9767 Time: 0.23\n",
      "Epoch: 476 Train Loss: 0.0899 Acc: 0.9247 Pre: 0.9289 Recall: 0.9198 F1: 0.9243 Train AUC: 0.9931 Val AUC: 0.9709 Val PRC: 0.9743 Time: 0.23\n",
      "Epoch: 477 Train Loss: 0.0941 Acc: 0.9295 Pre: 0.9416 Recall: 0.9159 F1: 0.9286 Train AUC: 0.9914 Val AUC: 0.9709 Val PRC: 0.9746 Time: 0.23\n",
      "Epoch: 478 Train Loss: 0.0854 Acc: 0.9315 Pre: 0.9509 Recall: 0.9100 F1: 0.9300 Train AUC: 0.9934 Val AUC: 0.9735 Val PRC: 0.9775 Time: 0.24\n",
      "Epoch: 479 Train Loss: 0.0857 Acc: 0.9266 Pre: 0.9413 Recall: 0.9100 F1: 0.9254 Train AUC: 0.9931 Val AUC: 0.9751 Val PRC: 0.9781 Time: 0.23\n",
      "Epoch: 480 Train Loss: 0.0805 Acc: 0.9305 Pre: 0.9400 Recall: 0.9198 F1: 0.9298 Train AUC: 0.9934 Val AUC: 0.9756 Val PRC: 0.9780 Time: 0.24\n",
      "Epoch: 481 Train Loss: 0.0910 Acc: 0.9188 Pre: 0.9131 Recall: 0.9256 F1: 0.9193 Train AUC: 0.9907 Val AUC: 0.9725 Val PRC: 0.9721 Time: 0.24\n",
      "Epoch: 482 Train Loss: 0.0752 Acc: 0.9237 Pre: 0.9597 Recall: 0.8845 F1: 0.9206 Train AUC: 0.9948 Val AUC: 0.9732 Val PRC: 0.9755 Time: 0.24\n",
      "Epoch: 483 Train Loss: 0.0916 Acc: 0.9198 Pre: 0.9248 Recall: 0.9139 F1: 0.9193 Train AUC: 0.9918 Val AUC: 0.9731 Val PRC: 0.9752 Time: 0.23\n",
      "Epoch: 484 Train Loss: 0.0916 Acc: 0.9207 Pre: 0.9072 Recall: 0.9374 F1: 0.9220 Train AUC: 0.9908 Val AUC: 0.9699 Val PRC: 0.9714 Time: 0.23\n",
      "Epoch: 485 Train Loss: 0.0731 Acc: 0.9237 Pre: 0.9339 Recall: 0.9119 F1: 0.9228 Train AUC: 0.9953 Val AUC: 0.9714 Val PRC: 0.9743 Time: 0.24\n",
      "Epoch: 486 Train Loss: 0.0765 Acc: 0.9207 Pre: 0.9216 Recall: 0.9198 F1: 0.9207 Train AUC: 0.9945 Val AUC: 0.9715 Val PRC: 0.9761 Time: 0.24\n",
      "Epoch: 487 Train Loss: 0.0764 Acc: 0.9237 Pre: 0.9204 Recall: 0.9276 F1: 0.9240 Train AUC: 0.9947 Val AUC: 0.9705 Val PRC: 0.9755 Time: 0.23\n",
      "Epoch: 488 Train Loss: 0.0769 Acc: 0.9276 Pre: 0.9468 Recall: 0.9061 F1: 0.9260 Train AUC: 0.9937 Val AUC: 0.9749 Val PRC: 0.9779 Time: 0.23\n",
      "Epoch: 489 Train Loss: 0.0818 Acc: 0.9266 Pre: 0.9395 Recall: 0.9119 F1: 0.9255 Train AUC: 0.9937 Val AUC: 0.9726 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 490 Train Loss: 0.0755 Acc: 0.9237 Pre: 0.9558 Recall: 0.8885 F1: 0.9209 Train AUC: 0.9950 Val AUC: 0.9735 Val PRC: 0.9771 Time: 0.24\n",
      "Epoch: 491 Train Loss: 0.0845 Acc: 0.9256 Pre: 0.9324 Recall: 0.9178 F1: 0.9250 Train AUC: 0.9929 Val AUC: 0.9698 Val PRC: 0.9745 Time: 0.24\n",
      "Epoch: 492 Train Loss: 0.0767 Acc: 0.9276 Pre: 0.9600 Recall: 0.8924 F1: 0.9249 Train AUC: 0.9937 Val AUC: 0.9726 Val PRC: 0.9747 Time: 0.24\n",
      "Epoch: 493 Train Loss: 0.0801 Acc: 0.9198 Pre: 0.9351 Recall: 0.9022 F1: 0.9183 Train AUC: 0.9927 Val AUC: 0.9685 Val PRC: 0.9720 Time: 0.24\n",
      "Epoch: 494 Train Loss: 0.0839 Acc: 0.9286 Pre: 0.9679 Recall: 0.8865 F1: 0.9254 Train AUC: 0.9933 Val AUC: 0.9744 Val PRC: 0.9772 Time: 0.24\n",
      "Epoch: 495 Train Loss: 0.0925 Acc: 0.9315 Pre: 0.9437 Recall: 0.9178 F1: 0.9306 Train AUC: 0.9917 Val AUC: 0.9746 Val PRC: 0.9776 Time: 0.23\n",
      "Epoch: 496 Train Loss: 0.0726 Acc: 0.9237 Pre: 0.9409 Recall: 0.9041 F1: 0.9222 Train AUC: 0.9953 Val AUC: 0.9715 Val PRC: 0.9753 Time: 0.23\n",
      "Epoch: 497 Train Loss: 0.0912 Acc: 0.9217 Pre: 0.9234 Recall: 0.9198 F1: 0.9216 Train AUC: 0.9925 Val AUC: 0.9727 Val PRC: 0.9758 Time: 0.23\n",
      "Epoch: 498 Train Loss: 0.0825 Acc: 0.9217 Pre: 0.9371 Recall: 0.9041 F1: 0.9203 Train AUC: 0.9933 Val AUC: 0.9719 Val PRC: 0.9753 Time: 0.24\n",
      "Epoch: 499 Train Loss: 0.0766 Acc: 0.9217 Pre: 0.9319 Recall: 0.9100 F1: 0.9208 Train AUC: 0.9937 Val AUC: 0.9741 Val PRC: 0.9762 Time: 0.24\n",
      "Epoch: 500 Train Loss: 0.0793 Acc: 0.9149 Pre: 0.8926 Recall: 0.9432 F1: 0.9172 Train AUC: 0.9940 Val AUC: 0.9713 Val PRC: 0.9748 Time: 0.24\n",
      "Fold: 5 Best Epoch: 480 Val acc: 0.9305 Val Pre: 0.9400 Val Recall: 0.9198 Val F1: 0.9298 Val AUC: 0.9756 Val PRC: 0.9780\n",
      "## Training Finished !\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Acc [0.9354, 0.9335, 0.9413, 0.9374, 0.9305]\n",
      "Pre [0.9320, 0.9683, 0.9328, 0.9497, 0.94]\n",
      "Recall [0.9393, 0.8963, 0.9511, 0.9237, 0.9198]\n",
      "F1 [0.9357, 0.9309, 0.9419, 0.9365, 0.9298]\n",
      "Auc [0.9793, 0.9830, 0.9822, 0.9794, 0.9756]\n",
      "Prc [0.9818, 0.9841, 0.9846, 0.9813, 0.978]\n",
      " AUC mean: 0.9799, variance: 0.0026 \n",
      " Accuracy mean: 0.9356, variance: 0.0036 \n",
      " Precision mean: 0.9446, variance: 0.0135 \n",
      " Recall mean: 0.9260, variance: 0.0186 \n",
      " F1-score mean: 0.9350, variance: 0.0043 \n",
      " PRC mean: 0.9820, variance: 0.0024 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 
      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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "# device = torch.device('cpu')\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "# training parameters\n",
    "parser.add_argument('--seed', type=int, default=0, help='Random seed.')\n",
    "parser.add_argument('--epochs', type=int, default=500,\n",
    "                    help='Number of epochs to train.')\n",
    "parser.add_argument('--weight_decay', type=float, default=5e-4,\n",
    "                    help='Weight decay (L2 loss on parameters).')\n",
    "parser.add_argument('--dropout', type=float, default=0.1,\n",
    "                    help='Dropout rate (1 - keep probability).')\n",
    "parser.add_argument('--tot_updates',  type=int, default=1000,\n",
    "                        help='used for optimizer learning rate scheduling')\n",
    "parser.add_argument('--warmup_updates', type=int, default=400,\n",
    "                        help='warmup steps')\n",
    "parser.add_argument('--peak_lr', type=float, default=0.001,  \n",
    "                        help='Initial learning rate')\n",
    "parser.add_argument('--end_lr', type=float, default=0.0001,  \n",
    "                        help='Final learning rate')\n",
    "# model parameters\n",
    "parser.add_argument('--pe_dim', type=int, default=15,\n",
    "                        help='position embedding size')\n",
    "parser.add_argument('--hops', type=int, default=7,\n",
    "                        help='Hop of neighbors to be calculated')\n",
    "parser.add_argument('--graphformer_layers', type=int, default=1,\n",
    "                    help='number of Graphormer layers')\n",
    "parser.add_argument('--n_heads', type=int, default=8,\n",
    "                    help='number of attention heads in Rgcgt.')\n",
    "parser.add_argument('--node_input', type=int, default=64,\n",
    "                    help='input dimensions of node features/PCA.')\n",
    "parser.add_argument('--node_hidden', type=int, default=128,  \n",
    "                    help='hidden dimensions of node features.')\n",
    "parser.add_argument('--node_output', type=int, default=64,  \n",
    "                    help='output dimensions of node features.')\n",
    "parser.add_argument('--ffn_dim', type=int, default=256,\n",
    "                        help='FFN layer size')\n",
    "parser.add_argument('--GCNII_layers', type=int, default=20,\n",
    "                    help='number of GCNII layers .')\n",
    "# Use parse_known_args to ignore unknown args\n",
    "args, unknown = parser.parse_known_args()\n",
    "# args = parser.parse_args()\n",
    "print('args', args)\n",
    "\n",
    "Adj, Dis_adj, Meta_adj, feature, random_index, k_folds = load_data(args.seed, args.node_input)\n",
    "\n",
    "auc_result = []\n",
    "acc_result = []\n",
    "pre_result = []\n",
    "recall_result = []\n",
    "f1_result = []\n",
    "prc_result = []\n",
    "fprs = []\n",
    "tprs = []\n",
    "precisions = []\n",
    "recalls = []\n",
    "print(\"seed=%d, evaluating metabolite-disease....\" % args.seed)\n",
    "for k in range(k_folds):\n",
    "    print(\"------this is %dth cross validation------\" % (k + 1))\n",
    "    Or_train = np.matrix(Adj, copy=True)\n",
    "    val_pos_edge_index = np.array(random_index[k]).T\n",
    "    val_pos_edge_index = torch.tensor(val_pos_edge_index, dtype=torch.long).to(device)\n",
    "    # Negative sampling of validation set\n",
    "    val_neg_edge_index = np.mat(np.where(Or_train < 1)).T.tolist()\n",
    "    random.seed(args.seed)\n",
    "    random.shuffle(val_neg_edge_index)\n",
    "    val_neg_edge_index = val_neg_edge_index[:val_pos_edge_index.shape[1]]\n",
    "    val_neg_edge_index = np.array(val_neg_edge_index).T\n",
    "    val_neg_edge_index = torch.tensor(val_neg_edge_index, dtype=torch.long).to(device)\n",
    "\n",
    "    Or_train[tuple(np.array(random_index[k]).T)] = 0\n",
    "    train_pos_edge_index = np.mat(np.where(Or_train > 0))\n",
    "    train_pos_edge_index = torch.tensor(train_pos_edge_index, dtype=torch.long).to(device)\n",
    "    Or_train_matrix = np.matrix(Adj, copy=True)\n",
    "    Or_train_matrix[tuple(np.array(random_index[k]).T)] = 0\n",
    "    or_adj = constructNet(torch.tensor(Or_train_matrix)).to(device)\n",
    "\n",
    "    # Positional encoding\n",
    "    lpe = laplacian_positional_encoding(or_adj, args.pe_dim).to(device)  # args.pe_dim: Positional encoding dimension\n",
    "    features = torch.cat((feature, lpe), dim=1)  # Equation (16)\n",
    "\n",
    "    # Construct disease similarity network\n",
    "    Dis_network = torch.nonzero(Dis_adj, as_tuple=True)\n",
    "    Dis_network = torch.stack(Dis_network)\n",
    "    dis_data = Data(x=feature[:Adj.shape[1], ], edge_index=Dis_network)\n",
    "\n",
    "    # Construct metabolite similarity network\n",
    "    Meta_network = torch.nonzero(Meta_adj, as_tuple=True)\n",
    "    Meta_network = torch.stack(Meta_network)\n",
    "    meta_data = Data(x=feature[Adj.shape[1]:, ], edge_index=Meta_network)\n",
    "\n",
    "    # Node embedding for t-hop neighbor aggregation\n",
    "    processed_features = re_features(or_adj, features, args.hops).to(device)  # return (N, hops+1, d)\n",
    "\n",
    "    model = TransformerModel(hops=args.hops,\n",
    "                             output_dim=args.node_output,\n",
    "                             input_dim=features.shape[1],\n",
    "                             pe_dim=args.pe_dim,\n",
    "                             num_dis=Adj.shape[1],\n",
    "                             num_meta=Adj.shape[0],\n",
    "                             graphformer_layers=args.graphformer_layers,\n",
    "                             num_heads=args.n_heads,\n",
    "                             hidden_dim=args.node_hidden,\n",
    "                             ffn_dim=args.ffn_dim,\n",
    "                             dropout_rate=args.dropout,\n",
    "                             GCNII_layers=args.GCNII_layers\n",
    "                             ).to(device)\n",
    "    # print(model)\n",
    "    print('total params:', sum(p.numel() for p in model.parameters()))\n",
    "    model.to(device)\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=args.peak_lr, weight_decay=args.weight_decay)\n",
    "    lr_scheduler = PolynomialDecayLR(\n",
    "        optimizer,\n",
    "        warmup_updates=args.warmup_updates,\n",
    "        tot_updates=args.tot_updates,\n",
    "        lr=args.peak_lr,\n",
    "        end_lr=args.end_lr,\n",
    "        power=1.0,\n",
    "    )\n",
    "    criterion = F.binary_cross_entropy\n",
    "    best_epoch = 0\n",
    "    best_auc = 0\n",
    "    best_acc = 0\n",
    "    best_prc = 0\n",
    "    best_tpr = 0\n",
    "    best_fpr = 0\n",
    "    best_recall = 0\n",
    "    best_precision = 0\n",
    "    for epoch in range(args.epochs):\n",
    "        start = time.time()\n",
    "\n",
    "        model.train()\n",
    "        optimizer.zero_grad()\n",
    "        train_neg_edge_index = np.mat(np.where(Or_train_matrix < 1)).T.tolist()\n",
    "        random.shuffle(train_neg_edge_index)\n",
    "        train_neg_edge_index = train_neg_edge_index[:train_pos_edge_index.shape[1]]\n",
    "        train_neg_edge_index = np.array(train_neg_edge_index).T\n",
    "        train_neg_edge_index = torch.tensor(train_neg_edge_index, dtype=torch.long).to(device)\n",
    "\n",
    "        output = model(processed_features, dis_data, meta_data).to(device)\n",
    "\n",
    "        edge_index = torch.cat([train_pos_edge_index, train_neg_edge_index], 1)\n",
    "        trian_scores = output[edge_index[0], edge_index[1]].to(device)\n",
    "        trian_labels = get_link_labels(train_pos_edge_index, train_neg_edge_index).to(device)\n",
    "        loss_train = criterion(trian_scores, trian_labels).to(device)\n",
    "        loss_train.backward(retain_graph=True)\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            score_train_cpu = np.squeeze(trian_scores.detach().cpu().numpy())\n",
    "            label_train_cpu = np.squeeze(trian_labels.detach().cpu().numpy())\n",
    "            train_auc = metrics.roc_auc_score(label_train_cpu, score_train_cpu)\n",
    "\n",
    "            predict_y_proba = output.reshape(Adj.shape[0], Adj.shape[1]).to(device)\n",
    "            score_val, label_val, metric_tmp = cv_model_evaluate(predict_y_proba, val_pos_edge_index, val_neg_edge_index)\n",
    "\n",
    "            fpr, tpr, thresholds = metrics.roc_curve(label_val, score_val)\n",
    "            precision, recall, _ = metrics.precision_recall_curve(label_val, score_val)\n",
    "            val_auc = metrics.auc(fpr, tpr)\n",
    "            val_prc = metrics.auc(recall, precision)\n",
    "\n",
    "            end = time.time()\n",
    "            print('Epoch:', epoch + 1, 'Train Loss: %.4f' % loss_train.item(),\n",
    "                  'Acc: %.4f' % metric_tmp[0], 'Pre: %.4f' % metric_tmp[1], 'Recall: %.4f' % metric_tmp[2],\n",
    "                  'F1: %.4f' % metric_tmp[3],\n",
    "                  'Train AUC: %.4f' % train_auc, 'Val AUC: %.4f' % val_auc, 'Val PRC: %.4f' % val_prc,\n",
    "                  'Time: %.2f' % (end - start))\n",
    "            if metric_tmp[0] > best_acc and val_auc > best_auc and val_prc > best_prc:\n",
    "                metric_tmp_best = metric_tmp\n",
    "                best_auc = val_auc\n",
    "                best_prc = val_prc\n",
    "                best_epoch = epoch + 1\n",
    "                best_tpr = tpr\n",
    "                best_fpr = fpr\n",
    "                best_recall = recall\n",
    "                best_precision = precision\n",
    "    print('Fold:', k + 1, 'Best Epoch:', best_epoch, 'Val acc: %.4f' % metric_tmp_best[0],\n",
    "              'Val Pre: %.4f' % metric_tmp_best[1],\n",
    "              'Val Recall: %.4f' % metric_tmp_best[2], 'Val F1: %.4f' % metric_tmp_best[3], 'Val AUC: %.4f' % best_auc,\n",
    "              'Val PRC: %.4f' % best_prc,\n",
    "              )\n",
    "\n",
    "    acc_result.append(metric_tmp_best[0])\n",
    "    pre_result.append(metric_tmp_best[1])\n",
    "    recall_result.append(metric_tmp_best[2])\n",
    "    f1_result.append(metric_tmp_best[3])\n",
    "    auc_result.append(round(best_auc, 4))\n",
    "    prc_result.append(round(best_prc, 4))\n",
    "\n",
    "    fprs.append(best_fpr)\n",
    "    tprs.append(best_tpr)\n",
    "    recalls.append(best_recall)\n",
    "    precisions.append(best_precision)\n",
    "\n",
    "print('## Training Finished !')\n",
    "print('-----------------------------------------------------------------------------------------------')\n",
    "print('Acc', acc_result)\n",
    "print('Pre', pre_result)\n",
    "print('Recall', recall_result)\n",
    "print('F1', f1_result)\n",
    "print('Auc', auc_result)\n",
    "print('Prc', prc_result)\n",
    "print('AUC mean: %.4f, variance: %.4f \\n' % (np.mean(auc_result), np.std(auc_result)),\n",
    "        'Accuracy mean: %.4f, variance: %.4f \\n' % (np.mean(acc_result), np.std(acc_result)),\n",
    "        'Precision mean: %.4f, variance: %.4f \\n' % (np.mean(pre_result), np.std(pre_result)),\n",
    "        'Recall mean: %.4f, variance: %.4f \\n' % (np.mean(recall_result), np.std(recall_result)),\n",
    "        'F1-score mean: %.4f, variance: %.4f \\n' % (np.mean(f1_result), np.std(f1_result)),\n",
    "        'PRC mean: %.4f, variance: %.4f \\n' % (np.mean(prc_result), np.std(prc_result)))\n",
    "\n",
    "plot_auc_curves(fprs, tprs, auc_result, directory='../result', name='test_auc')\n",
    "plot_prc_curves(precisions, recalls, prc_result, directory='../result', name='test_prc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bd388e-7836-42dd-a176-be43ef115d79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "155c5e07-fc05-4a59-971c-1833edbcda0a",
   "metadata": {},
   "outputs": [],
   "source": []
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